{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":1999,"total_is_capped":false,"direct_labels_cover":2,"predictions_cover":1999,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"4e46d8a7834e","filters":{"topic":"Network Security and Intrusion Detection"}},"results":[{"id":"W2099940443","doi":"10.1109/cisda.2009.5356528","title":"A detailed analysis of the KDD CUP 99 data set","year":2009,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":4782,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada; University of New Brunswick","funders":"","keywords":"Computer science; Data mining; Anomaly detection; Set (abstract data type); Data set; Signature (topology); Anomaly (physics); Knowledge extraction; Artificial intelligence; Mathematics","authors":[{"name":"Mahbod Tavallaee","is_ca":true},{"name":"Ebrahim Bagheri","is_ca":true},{"name":"Wei Lu","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03366468587413417,"gpt":0.271417510805663,"spread":0.2377528249315288,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002040506,0.0007837804,0.0009710014,0.007708716,0.001643196,0.002135677,0.001000074,0.000695203,0.002273865],"category_scores_gemma":[0.006602654,0.0001793965,0.0008519328,0.008623339,0.0004397916,0.001334203,0.0007479417,0.0007920258,0.002023547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001405325,"about_ca_system_score_gemma":0.002169768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01739994,"about_ca_topic_score_gemma":0.01994447,"domain_scores_codex":[0.9954991,0.000329385,0.0004371259,0.0003298997,0.003122622,0.0002818726],"domain_scores_gemma":[0.9945663,0.001124018,0.0002436997,0.0007543014,0.003103582,0.0002080711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000816441,0.001771719,0.07482187,0.001888488,0.0004146214,0.00126599,0.0007468329,0.02693634,0.007189075,0.01178811,0.4614237,0.4109368],"study_design_scores_gemma":[0.0001046044,0.000453284,0.3172847,0.0002837975,0.0001573832,0.002199728,0.002274158,0.1508334,0.02633228,0.008140933,0.4916162,0.0003195456],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.4455943,0.003768847,0.04403275,0.004840733,0.001933032,0.001720582,0.4487604,0.0075029,0.04184647],"genre_scores_gemma":[0.4458537,0.001911407,0.07758681,0.0004615679,0.0002240952,0.0009644905,0.4612133,0.0003165087,0.01146808],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01739994,"threshold_uncertainty_score":0.03459734,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2789828921","doi":"10.5220/0006639801080116","title":"Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization","year":2018,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":4265,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Intrusion detection system; Computer science; Intrusion; Data mining; Geology","authors":[{"name":"Iman Sharafaldin","is_ca":true},{"name":"Arash Habibi Lashkari","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02084551940616644,"gpt":0.2355546520635846,"spread":0.2147091326574182,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001903981,0.001704349,0.001228035,0.006217703,0.001190949,0.002148204,0.002370432,0.001459079,0.001351676],"category_scores_gemma":[0.007654609,0.0006851974,0.001553223,0.003144562,0.000558604,0.003384039,0.002138808,0.002549588,0.001773239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001641484,"about_ca_system_score_gemma":0.002950243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007371611,"about_ca_topic_score_gemma":0.01374459,"domain_scores_codex":[0.9981229,0.0002419929,0.0001892569,0.0006450494,0.0006024538,0.0001983759],"domain_scores_gemma":[0.9939107,0.0009718509,0.0005164081,0.001577427,0.002661408,0.0003622819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006363185,0.004134032,0.09237622,0.0009869785,0.0006018923,0.0009032712,0.0004303522,0.08882792,0.1088517,0.01507736,0.144919,0.5422549],"study_design_scores_gemma":[0.00009939235,0.0004737613,0.03728655,0.0001596352,0.000243487,0.000709687,0.0004022109,0.826619,0.05730809,0.01283765,0.06373889,0.0001217127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1669517,0.0005512801,0.7127197,0.002168651,0.0005393737,0.001818816,0.088374,0.02165979,0.005216711],"genre_scores_gemma":[0.1806506,0.0004781174,0.5723395,0.0006455804,0.0002628113,0.001464338,0.2405231,0.0006492414,0.00298669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007371611,"threshold_uncertainty_score":0.01465738,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2982682021","doi":"10.1109/ccst.2019.8888419","title":"Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy","year":2019,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1064,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Denial-of-service attack; Computer science; Application layer DDoS attack; Data mining; Taxonomy (biology); Computer security; Set (abstract data type); Overhead (engineering); Computer network; The Internet; World Wide Web","authors":[{"name":"Iman Sharafaldin","is_ca":true},{"name":"Arash Habibi Lashkari","is_ca":true},{"name":"Saqib Hakak","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04438545276565223,"gpt":0.2622455069141732,"spread":0.217860054148521,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001082115,0.0008369017,0.0005362467,0.00284081,0.0009536861,0.0009021126,0.001665631,0.001519236,0.0009425895],"category_scores_gemma":[0.003125324,0.0002380235,0.0006770868,0.002446882,0.0004946911,0.001354699,0.001061954,0.001604594,0.0006767294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001358725,"about_ca_system_score_gemma":0.001272651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007704581,"about_ca_topic_score_gemma":0.009978534,"domain_scores_codex":[0.9987711,0.0001651214,0.0001583762,0.0003057221,0.0004391048,0.000160542],"domain_scores_gemma":[0.9981074,0.0004030042,0.0002268922,0.0004390801,0.0006299863,0.0001935418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001277235,0.0042875,0.09212864,0.001909047,0.0003222307,0.00143339,0.0005502299,0.1552378,0.02273337,0.01913431,0.4186422,0.2823441],"study_design_scores_gemma":[0.0004465958,0.0009952814,0.08547484,0.0002529789,0.0001282161,0.002012502,0.001134052,0.5825672,0.03548797,0.009965907,0.2812877,0.0002467574],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.5950594,0.00192867,0.07564533,0.003874672,0.001095447,0.00336598,0.2935902,0.007907451,0.01753278],"genre_scores_gemma":[0.4167648,0.0008598601,0.08928723,0.0005747637,0.0001726966,0.001728599,0.4865316,0.000183242,0.003897174],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.007704581,"threshold_uncertainty_score":0.01531947,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4382281941","doi":"10.3390/s23135941","title":"CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":793,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Internet of Things; Computer security; Interoperability; Spoofing attack; Denial-of-service attack; Analytics; Benchmark (surveying); Data science; World Wide Web; The Internet","authors":[{"name":"Euclides Carlos Pinto Neto","is_ca":true},{"name":"Sajjad Dadkhah","is_ca":true},{"name":"Raphael Ferreira","is_ca":true},{"name":"Alireza Zohourian","is_ca":true},{"name":"Rongxing Lu","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01200129317143633,"gpt":0.2459253076235887,"spread":0.2339240144521524,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001647571,0.002336804,0.001208014,0.0036457,0.001256127,0.001797636,0.002894456,0.002884746,0.002074627],"category_scores_gemma":[0.006641855,0.0003865274,0.001442123,0.004395971,0.0008250401,0.002383332,0.001831433,0.002189762,0.003499958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001743093,"about_ca_system_score_gemma":0.001576537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01789662,"about_ca_topic_score_gemma":0.02352889,"domain_scores_codex":[0.9972849,0.0003389515,0.0003687029,0.0006000143,0.001012696,0.000394847],"domain_scores_gemma":[0.996197,0.0007649547,0.0005113563,0.001166387,0.0009264501,0.0004338635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009673503,0.0009421082,0.04185663,0.001507865,0.0004780677,0.001103201,0.0002713916,0.03633659,0.004486037,0.003099587,0.8639918,0.04495946],"study_design_scores_gemma":[0.0006439033,0.0008496298,0.1434299,0.0005190534,0.0003115519,0.003458084,0.001400088,0.2854799,0.0148588,0.007185429,0.5415345,0.0003291416],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.09707134,0.002550967,0.009429261,0.002208531,0.000975002,0.0008631852,0.8583829,0.0174731,0.01104568],"genre_scores_gemma":[0.07495771,0.0004495636,0.007698293,0.0003000165,0.00009901763,0.0003964815,0.9146707,0.0002843234,0.001143992],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01789662,"threshold_uncertainty_score":0.03558493,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3157680283","doi":"10.1109/jiot.2021.3077803","title":"Federated-Learning-Based Anomaly Detection for IoT Security Attacks","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":677,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brandon University; University of Guelph","funders":"","keywords":"Computer science; Anomaly detection; Internet of Things; Intrusion detection system; Web server; Server; The Internet; Edge device; Computer security; Artificial intelligence; Computer network; Machine learning; World Wide Web; Cloud computing; Operating system","authors":[{"name":"Viraaji Mothukuri","is_ca":false},{"name":"Prachi Khare","is_ca":false},{"name":"Reza M. Parizi","is_ca":false},{"name":"Seyedamin Pouriyeh","is_ca":false},{"name":"Ali Dehghantanha","is_ca":true},{"name":"Gautam Srivastava","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01255430019074486,"gpt":0.247869346967472,"spread":0.2353150467767271,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001302773,0.0009911172,0.001654855,0.000973547,0.0005433772,0.0008749549,0.001898975,0.0009384893,0.0005423532],"category_scores_gemma":[0.004032758,0.0003106671,0.0008099739,0.0007936807,0.0007169217,0.001925851,0.001521089,0.001710403,0.0002570434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009127976,"about_ca_system_score_gemma":0.001077575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003914312,"about_ca_topic_score_gemma":0.002912111,"domain_scores_codex":[0.9987061,0.0002287884,0.00008618929,0.0004172624,0.0003631675,0.0001984842],"domain_scores_gemma":[0.9981018,0.0006058514,0.000280956,0.0004145729,0.0004859627,0.0001109294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004105076,0.0004112893,0.009710735,0.00006720892,0.000171057,0.0002769389,0.0001350154,0.6320629,0.01022844,0.004264717,0.002882287,0.339379],"study_design_scores_gemma":[0.000003348684,0.00002595439,0.0002822735,0.000001914138,0.000006794936,0.00003189079,0.00000721797,0.9962867,0.001421118,0.001767697,0.0001601937,0.00000489785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08608568,0.0005913708,0.9067497,0.0003444672,0.0001228129,0.00004292861,0.00009679565,0.004940353,0.001025909],"genre_scores_gemma":[0.9537544,0.0001366999,0.04466558,0.0001509857,0.00004686006,0.00003419702,0.0002123055,0.00006376974,0.0009352993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003914312,"threshold_uncertainty_score":0.007783055,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2061686014","doi":"10.1145/2785733","title":"Collaborative Security","year":2015,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":417,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Security information and event management; Cloud computing security; Interoperability; Computer security; Security through obscurity; Variety (cybernetics); Security service; Vulnerability (computing); Intrusion detection system; Security engineering; Collaboration; Asset (computer security); Computer security model; Software security assurance; Information security; Knowledge management; World Wide Web; Cloud computing","authors":[{"name":"Guozhu Meng","is_ca":false},{"name":"Yang Liu","is_ca":false},{"name":"Jie Zhang","is_ca":false},{"name":"Alexander Pokluda","is_ca":true},{"name":"Raouf Boutaba","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05115314088231645,"gpt":0.336524387574975,"spread":0.2853712466926586,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003942516,0.001123147,0.0009085789,0.00290021,0.001911608,0.006991939,0.003023026,0.003866885,0.02237718],"category_scores_gemma":[0.007842536,0.0004724724,0.0009006891,0.003080394,0.003863714,0.01320899,0.005918031,0.003223026,0.01037071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003253547,"about_ca_system_score_gemma":0.00520236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001817564,"about_ca_topic_score_gemma":0.001702191,"domain_scores_codex":[0.9947811,0.001669662,0.0003504422,0.0008545834,0.001902424,0.0004418163],"domain_scores_gemma":[0.993569,0.002681381,0.0005808217,0.001541363,0.001154343,0.0004730771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002574243,0.00005926643,0.0005394456,0.003156245,0.00008719788,0.0001693029,0.001032358,0.0006548635,0.0006357065,0.4164768,0.05538601,0.521777],"study_design_scores_gemma":[0.000005981494,0.00002630698,0.0003433246,0.001436143,0.00002041005,0.0005193574,0.000297962,0.0002363513,0.0002909953,0.05382279,0.9429829,0.000017511],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003245134,0.4575501,0.06751286,0.01845851,0.003753417,0.0003894519,0.0002433856,0.0006657424,0.4481814],"genre_scores_gemma":[0.144418,0.6712726,0.03794766,0.01200776,0.004166367,0.0007325044,0.0008878895,0.0002909591,0.1282762],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02237718,"threshold_uncertainty_score":0.07485908,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3082551002","doi":"10.1109/mnet.011.2000286","title":"Internet of Things Intrusion Detection: Centralized, On-Device, or Federated Learning?","year":2020,"lang":"en","type":"article","venue":"IEEE Network","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":398,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Intrusion detection system; Robustness (evolution); Malware; Federated learning; Single point of failure; The Internet; Inference; Computer security; Context (archaeology); Server; Machine learning; Distributed computing; Computer network; Artificial intelligence; Data mining; World Wide Web","authors":[{"name":"Sawsan Abdul Rahman","is_ca":true},{"name":"Hanine Tout","is_ca":true},{"name":"Chamseddine Talhi","is_ca":true},{"name":"Azzam Mourad","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02391435482072456,"gpt":0.2371564820565921,"spread":0.2132421272358676,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004577708,0.00070008,0.001709297,0.0007537374,0.0008104074,0.002097422,0.002671589,0.002290325,0.0005875239],"category_scores_gemma":[0.007533268,0.0003254132,0.0007101176,0.0008496905,0.001702298,0.005723204,0.002144574,0.001522681,0.00027588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001151508,"about_ca_system_score_gemma":0.001149778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001557994,"about_ca_topic_score_gemma":0.002009336,"domain_scores_codex":[0.9967908,0.0009938458,0.0001580688,0.0009806573,0.0007151865,0.0003614119],"domain_scores_gemma":[0.9956085,0.001131976,0.0005300705,0.001951144,0.0005665267,0.0002118133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001028524,0.001193118,0.02425884,0.0002002862,0.0003593768,0.0003552065,0.0003420542,0.300047,0.00827926,0.02999048,0.006482681,0.6274632],"study_design_scores_gemma":[0.00003180537,0.0002123629,0.002158431,0.00002577872,0.00004418245,0.0003060851,0.0001222935,0.9637018,0.005444769,0.02670173,0.001223885,0.00002681795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1393108,0.001192439,0.8504303,0.002342942,0.0001915441,0.0001456884,0.0001018,0.002298204,0.003986401],"genre_scores_gemma":[0.9641426,0.0001886074,0.03429843,0.0003070219,0.00005966468,0.00003209166,0.00007109452,0.00002234496,0.0008782464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004577708,"threshold_uncertainty_score":0.02420956,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3179240416","doi":"10.1109/access.2021.3094024","title":"Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":387,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Anomaly detection; Intrusion detection system; Machine learning; Deep learning; Artificial neural network; Multiclass classification; Data mining; Support vector machine","authors":[{"name":"Imtiaz Ullah","is_ca":true},{"name":"Qusay H. Mahmoud","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03730834366227687,"gpt":0.2709730374959741,"spread":0.2336646938336973,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000410205,0.0005330627,0.0004345048,0.0004197755,0.0003378884,0.0005645082,0.001396108,0.0008080319,0.001180592],"category_scores_gemma":[0.0008214121,0.0004013404,0.0006244369,0.0003939429,0.0004119311,0.0009576269,0.0006057956,0.001219014,0.0003302598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00125256,"about_ca_system_score_gemma":0.001278656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01387812,"about_ca_topic_score_gemma":0.01102566,"domain_scores_codex":[0.9998001,0.00002254617,0.00001446661,0.00005901763,0.00006905713,0.00003490309],"domain_scores_gemma":[0.9997427,0.00006329431,0.00002888149,0.00002121425,0.0001263921,0.00001753621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005284167,0.00005910469,0.001431687,0.00003959486,0.00003070275,0.00006718728,0.00002590693,0.9293483,0.006792251,0.005992971,0.001020936,0.05513854],"study_design_scores_gemma":[9.210034e-7,0.000005335534,0.00003870046,0.000001091868,0.000001924467,0.000004359469,6.675046e-7,0.9987785,0.0005809715,0.0004295006,0.0001567337,0.000001289401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02379016,0.0001990629,0.9719039,0.0003564889,0.00006250724,0.00005764355,0.0001478109,0.001153782,0.002328697],"genre_scores_gemma":[0.7694679,0.0004174256,0.2228541,0.0002401634,0.00004280733,0.0002602634,0.0004878818,0.0001133263,0.006115987],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01387812,"threshold_uncertainty_score":0.02759463,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3164321464","doi":"10.1109/jiot.2021.3084796","title":"MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":387,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Computer science; Intrusion detection system; Network packet; Anomaly detection; The Internet; Computer network; Real-time computing; Computer security; Artificial intelligence","authors":[{"name":"Li Yang","is_ca":true},{"name":"Abdallah Moubayed","is_ca":true},{"name":"Abdallah Shami","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01403622539075175,"gpt":0.2323286713777449,"spread":0.2182924459869932,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001028091,0.0009020671,0.001026065,0.00146267,0.0004075535,0.000809354,0.001772566,0.0007746092,0.001050223],"category_scores_gemma":[0.001520512,0.0003446269,0.0004667028,0.0004650388,0.0003669959,0.001466752,0.001558661,0.0008010616,0.0006688878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009199668,"about_ca_system_score_gemma":0.0008262592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002396767,"about_ca_topic_score_gemma":0.001807286,"domain_scores_codex":[0.9991622,0.0001530146,0.00008895023,0.0002300707,0.0002752685,0.00009045772],"domain_scores_gemma":[0.9994273,0.0001077491,0.00009562172,0.000109405,0.0002013299,0.00005853166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001998714,0.0007973862,0.02696803,0.000546133,0.0007226146,0.0005826127,0.0003905647,0.1292615,0.08001284,0.007620666,0.0307712,0.7203277],"study_design_scores_gemma":[0.00006379661,0.0003678486,0.002606194,0.00001347989,0.00007058081,0.0002368953,0.00004266722,0.9604036,0.02597857,0.001650624,0.008509835,0.00005595587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1251782,0.001413794,0.7811656,0.0004099507,0.0006753876,0.0005795783,0.001227324,0.08538358,0.003966492],"genre_scores_gemma":[0.8131419,0.0003274877,0.1801078,0.000298742,0.00008184429,0.0002660124,0.002164183,0.0002483377,0.003363682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002396767,"threshold_uncertainty_score":0.006674886,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3047132966","doi":"10.1016/j.jnca.2020.102767","title":"Deep learning methods in network intrusion detection: A survey and an objective comparison","year":2020,"lang":"en","type":"article","venue":"Journal of Network and Computer Applications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":366,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Deep learning; Artificial intelligence; Machine learning; Autoencoder; Intrusion detection system; Inference; Deep belief network; Artificial neural network; Task (project management); Data mining","authors":[{"name":"Sunanda Gamage","is_ca":true},{"name":"Jagath Samarabandu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02767580220436236,"gpt":0.3151994203677254,"spread":0.287523618163363,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007456926,0.001943404,0.002371482,0.003727452,0.0004018103,0.002318822,0.001958925,0.001568511,0.001719766],"category_scores_gemma":[0.01197128,0.0005913923,0.001096389,0.004205004,0.0008063344,0.00434021,0.001831767,0.00209812,0.0005812623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064324,"about_ca_system_score_gemma":0.001349466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002574299,"about_ca_topic_score_gemma":0.002895817,"domain_scores_codex":[0.9959079,0.001100073,0.0004731278,0.0005742561,0.001779663,0.0001650326],"domain_scores_gemma":[0.9912881,0.0058015,0.000533744,0.0005407543,0.001631479,0.000204389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004484721,0.0003367435,0.009416825,0.002158347,0.0005843001,0.00003424922,0.00006279321,0.04247874,0.0009213075,0.007899344,0.005937141,0.9297218],"study_design_scores_gemma":[0.0001353419,0.001269362,0.01352277,0.002323958,0.0009178642,0.0005093231,0.0002876197,0.8983498,0.007083363,0.03545249,0.04001895,0.0001291636],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0421828,0.3665099,0.5757562,0.003295389,0.0008279269,0.0002484089,0.001048226,0.0008126558,0.009318558],"genre_scores_gemma":[0.4345784,0.3245888,0.2277244,0.00148706,0.001911219,0.0004238568,0.002491679,0.0003684,0.006426172],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.007456926,"threshold_uncertainty_score":0.03943646,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4206713037","doi":"10.3390/electronics11020198","title":"Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review","year":2022,"lang":"en","type":"article","venue":"Electronics","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":350,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Yayasan UTP; Universiti Teknologi Petronas; King Saud University","keywords":"Computer science; Computer security; Intrusion detection system; Artificial intelligence; Machine learning; Support vector machine; The Internet; Random forest; World Wide Web","authors":[{"name":"Mujaheed Abdullahi","is_ca":false},{"name":"Yahia Baashar","is_ca":false},{"name":"Hitham Alhussian","is_ca":false},{"name":"Ayed Alwadain","is_ca":false},{"name":"Norshakirah Aziz","is_ca":false},{"name":"Luiz Fernando Capretz","is_ca":true},{"name":"Said Jadid Abdulkadir","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02913918030108004,"gpt":0.3182235669894918,"spread":0.2890843866884117,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006040252,0.001286032,0.003476813,0.01648399,0.0007872079,0.002742946,0.001748427,0.001728738,0.003883539],"category_scores_gemma":[0.02964961,0.0008424416,0.004307649,0.01378612,0.0009881129,0.003028131,0.001560565,0.001209116,0.0004771265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002545875,"about_ca_system_score_gemma":0.01332467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006445134,"about_ca_topic_score_gemma":0.01825847,"domain_scores_codex":[0.9944485,0.001606745,0.002122308,0.000395311,0.001273113,0.0001539213],"domain_scores_gemma":[0.9660175,0.0274822,0.00330136,0.0004220962,0.002553732,0.0002231394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00007091089,0.00004782161,0.00134064,0.8548474,0.001978306,0.0002476327,0.0006021574,0.0002442809,0.0002171238,0.001037695,0.002560215,0.1368058],"study_design_scores_gemma":[0.0000343206,0.0001429426,0.002717071,0.939487,0.01063045,0.0005350501,0.0007175798,0.0001590023,0.0002266941,0.0006755331,0.04463803,0.00003625652],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001058423,0.9968857,0.000416508,0.0003934728,0.00008766638,0.0002695308,0.0002758034,0.000007830612,0.0006050295],"genre_scores_gemma":[0.006082907,0.9920434,0.000883986,0.0003612925,0.00004759217,0.0003233549,0.000158785,0.000004559827,0.0000941197],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01648399,"threshold_uncertainty_score":0.03194427,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2991435551","doi":"10.1002/ett.3803","title":"DL‐IDS: a deep learning–based intrusion detection framework for securing IoT","year":2019,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":335,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Intrusion detection system; Denial-of-service attack; Computer network; Computer security; Artificial intelligence; Internet of Things; Deep learning; Wireless network; Wearable computer; Wireless; Machine learning; The Internet; Embedded system; Telecommunications","authors":[{"name":"Yazan Otoum","is_ca":true},{"name":"Dandan Liu","is_ca":false},{"name":"Amiya Nayak","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01281290440659908,"gpt":0.2578505190139471,"spread":0.245037614607348,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007859235,0.0007887035,0.000739028,0.000689649,0.0002599417,0.0006062323,0.00149684,0.0007841064,0.001144697],"category_scores_gemma":[0.001354651,0.0003473205,0.0007089944,0.0004203279,0.0005124486,0.0009732943,0.001162532,0.001358701,0.0003395314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008320537,"about_ca_system_score_gemma":0.001057483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004945707,"about_ca_topic_score_gemma":0.00498378,"domain_scores_codex":[0.999635,0.0000696565,0.00002837884,0.00009872511,0.00009906341,0.00006918485],"domain_scores_gemma":[0.9996427,0.0001159419,0.0000514998,0.00003480679,0.0001228945,0.0000322025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002788062,0.0003558878,0.006192641,0.0001692095,0.000209346,0.000194328,0.00008991904,0.6295966,0.009750061,0.008896597,0.009835729,0.3344309],"study_design_scores_gemma":[0.000003355621,0.00001677038,0.0001168443,0.000003161075,0.000005569042,0.000009059581,0.000002746737,0.9973514,0.0008359074,0.001293682,0.0003587636,0.000002834915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02758452,0.0007183562,0.9650155,0.0005155114,0.00009197752,0.00006750759,0.000242313,0.004437418,0.001326832],"genre_scores_gemma":[0.7065277,0.0005695376,0.2865138,0.0007691453,0.00009057695,0.000194145,0.001077078,0.0001271276,0.004130809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004945707,"threshold_uncertainty_score":0.009833813,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3081430061","doi":"10.1109/access.2020.3019330","title":"A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":304,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"CYTED Ciencia y Tecnología para el Desarrollo; Instituto Tecnológico y de Estudios Superiores de Monterrey; University of Texas at San Antonio","keywords":"Computer science; Denial-of-service attack; Intrusion detection system; Software-defined networking; Support vector machine; Application layer DDoS attack; Computer network; Testbed; Random forest; Machine learning; Artificial intelligence; Computer security; Operating system; The Internet","authors":[{"name":"Jesús Arturo Pérez-Díaz","is_ca":false},{"name":"Ismael Amezcua Valdovinos","is_ca":false},{"name":"Kim‐Kwang Raymond Choo","is_ca":false},{"name":"Dakai Zhu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07119681957159003,"gpt":0.3258249427423021,"spread":0.2546281231707121,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008343394,0.0004307334,0.0003850444,0.0005472912,0.0003187687,0.0005703247,0.001272859,0.0004249397,0.0009325623],"category_scores_gemma":[0.00101326,0.0002092293,0.0003203899,0.0003194675,0.0003640866,0.001025009,0.0006973064,0.0006616751,0.0004012679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004157458,"about_ca_system_score_gemma":0.000598361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001804371,"about_ca_topic_score_gemma":0.002432745,"domain_scores_codex":[0.9996901,0.00004557888,0.00002548102,0.0001051328,0.00009150602,0.00004222193],"domain_scores_gemma":[0.9995224,0.00008834475,0.00006669457,0.0001015148,0.0001771923,0.00004382752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005892449,0.0005961091,0.0114971,0.000206032,0.000207905,0.0002164181,0.0001391777,0.4667332,0.07122986,0.008498196,0.006112018,0.4339746],"study_design_scores_gemma":[0.00001822878,0.0001464516,0.001076639,0.00001115882,0.00002982422,0.00006743732,0.00001071124,0.9843773,0.01037108,0.001971423,0.001904204,0.0000156147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09231091,0.00041533,0.8970851,0.0003133832,0.0001442793,0.0001228868,0.0001692784,0.006538836,0.002899916],"genre_scores_gemma":[0.8339884,0.0001644807,0.1636935,0.0001912794,0.000046216,0.00009024342,0.0003317665,0.00005587748,0.001438156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001804371,"threshold_uncertainty_score":0.004412472,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2901492899","doi":"10.1016/j.comnet.2018.11.010","title":"Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection","year":2018,"lang":"en","type":"article","venue":"Computer Networks","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":304,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Computer science; Dimensionality reduction; Artificial intelligence; Pattern recognition (psychology); Intrusion detection system; Principal component analysis; Support vector machine; Classifier (UML); Anomaly detection; False positive rate; Data mining; Machine learning; Constant false alarm rate; Multilayer perceptron; Curse of dimensionality; Artificial neural network","authors":[{"name":"Fadi Salo","is_ca":true},{"name":"Ali Bou Nassif","is_ca":true},{"name":"Aleksander Essex","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01315722400387469,"gpt":0.2222964383563407,"spread":0.209139214352466,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00105498,0.0009746589,0.001348366,0.001584676,0.0009172247,0.001033832,0.001040605,0.0007370148,0.001819195],"category_scores_gemma":[0.002263718,0.0002891727,0.001942219,0.001957176,0.0002557632,0.00116334,0.0009038489,0.001662617,0.001025203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004645862,"about_ca_system_score_gemma":0.001235314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006368001,"about_ca_topic_score_gemma":0.005833738,"domain_scores_codex":[0.9985689,0.0002686987,0.00009641885,0.0002953234,0.00058156,0.0001890907],"domain_scores_gemma":[0.9991775,0.0001358391,0.00003735494,0.0001729042,0.0004474822,0.00002877024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002149648,0.0002801298,0.00385877,0.00009469204,0.0002409191,0.00009584589,0.000121415,0.05424335,0.01283539,0.003203908,0.01102019,0.9137904],"study_design_scores_gemma":[0.000009935889,0.00008236243,0.004250953,0.00001191624,0.0000852545,0.0001180314,0.000047398,0.9823353,0.007320867,0.002825335,0.002885829,0.00002683215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05811949,0.001246332,0.9333399,0.0003334783,0.0004020838,0.0001460017,0.0005947726,0.003129903,0.002688007],"genre_scores_gemma":[0.5145624,0.001109648,0.4733011,0.0001482356,0.0003078759,0.0003413623,0.002880858,0.0002034935,0.00714509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006368001,"threshold_uncertainty_score":0.01266187,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3022604549","doi":"10.1007/978-3-030-47358-7_52","title":"A Scheme for Generating a Dataset for Anomalous Activity Detection in IoT Networks","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":298,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Botnet; Internet of Things; Intrusion detection system; Scheme (mathematics); Data mining; Set (abstract data type); The Internet; Artificial intelligence; Computer security; World Wide Web","authors":[{"name":"Imtiaz Ullah","is_ca":true},{"name":"Qusay H. Mahmoud","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02273888662673615,"gpt":0.252053421875638,"spread":0.2293145352489018,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00170712,0.0009578308,0.0009216009,0.0029116,0.0009861192,0.001834788,0.002034954,0.001418475,0.003218189],"category_scores_gemma":[0.005503506,0.0005637243,0.001165034,0.002668631,0.0003707058,0.002347216,0.002419874,0.001689046,0.003241705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000909354,"about_ca_system_score_gemma":0.001794609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002518169,"about_ca_topic_score_gemma":0.004615343,"domain_scores_codex":[0.998459,0.0001638334,0.0002318778,0.0004240092,0.0006051897,0.0001161027],"domain_scores_gemma":[0.9962919,0.0007088549,0.0002772694,0.001798433,0.0007492539,0.0001741905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001151783,0.001014825,0.01940096,0.0006034902,0.0003076878,0.0008082136,0.0003812989,0.04914048,0.08284389,0.02066354,0.1102398,0.7134441],"study_design_scores_gemma":[0.0001465713,0.0003148564,0.009895876,0.00008297651,0.00009095822,0.0008783362,0.0001869059,0.8582483,0.05513427,0.02627692,0.04862225,0.0001217836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02074656,0.0001928566,0.9158563,0.0004250162,0.0002664352,0.001342404,0.02815053,0.03103931,0.001980703],"genre_scores_gemma":[0.1097396,0.0001645784,0.823989,0.0001864226,0.0001011683,0.001441965,0.06133369,0.0004917154,0.002551868],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003218189,"threshold_uncertainty_score":0.01076597,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2959716986","doi":"10.1109/tnsm.2019.2927886","title":"A Hybrid Deep Learning-Based Model for Anomaly Detection in Cloud Datacenter Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":284,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Anomaly detection; Data mining; Benchmark (surveying); Cloud computing; Convolutional neural network; Artificial intelligence; Anomaly (physics); Data modeling; False positive paradox; Data set; Machine learning","authors":[{"name":"Sahil Garg","is_ca":true},{"name":"Kuljeet Kaur","is_ca":true},{"name":"Neeraj Kumar","is_ca":false},{"name":"Georges Kaddoum","is_ca":true},{"name":"Albert Y. Zomaya","is_ca":false},{"name":"Rajiv Ranjan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009220484267888218,"gpt":0.2057793432185385,"spread":0.1965588589506503,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005924595,0.0006115207,0.0007075833,0.0004966003,0.0002727864,0.0007358099,0.001611243,0.0008442481,0.0007789618],"category_scores_gemma":[0.001225694,0.0003651835,0.0005260442,0.0005384738,0.0005541193,0.001018519,0.0006786456,0.001193134,0.0001297232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00127963,"about_ca_system_score_gemma":0.001027367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02010375,"about_ca_topic_score_gemma":0.01447317,"domain_scores_codex":[0.9997804,0.00003653012,0.0000114242,0.00007046739,0.00004688182,0.00005434295],"domain_scores_gemma":[0.9996614,0.0001416103,0.00004884334,0.00001808309,0.0001076511,0.00002235073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000045768,0.00002502402,0.00113052,0.00001547996,0.00001733171,0.00003558143,0.00001616464,0.9785451,0.0007916329,0.002050194,0.0003989133,0.01692833],"study_design_scores_gemma":[5.282857e-7,0.000001977035,0.00002959732,4.740968e-7,8.571319e-7,0.000001579961,4.538109e-7,0.999653,0.00005534762,0.0002341567,0.00002150261,5.214503e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1061242,0.0006473372,0.8892425,0.0005422471,0.00007056713,0.00004522244,0.0002065869,0.0008042838,0.002316967],"genre_scores_gemma":[0.9619622,0.0002294775,0.03424791,0.0001487556,0.00002877027,0.00006679069,0.0002014709,0.00004158574,0.003073013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02010375,"threshold_uncertainty_score":0.0399735,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2103414007","doi":"10.1109/tsmcc.2010.2048428","title":"Toward Credible Evaluation of Anomaly-Based Intrusion-Detection Methods","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":280,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Intrusion detection system; Anomaly detection; Anomaly-based intrusion detection system; Variety (cybernetics); Constant false alarm rate; Computer science; Anomaly (physics); Data mining; Field (mathematics); Data science; Mainstream; Computer security; Artificial intelligence; Mathematics","authors":[{"name":"Mahbod Tavallaee","is_ca":true},{"name":"Natalia Stakhanova","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04866288168365428,"gpt":0.3191304410893022,"spread":0.2704675594056479,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08971536,0.001779818,0.002366085,0.008963393,0.001044942,0.006128384,0.004535339,0.003379176,0.001424747],"category_scores_gemma":[0.320644,0.0008369077,0.0008583856,0.003751632,0.002849357,0.008378476,0.003156997,0.003420909,0.000812295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002651736,"about_ca_system_score_gemma":0.002958816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009486073,"about_ca_topic_score_gemma":0.0007589032,"domain_scores_codex":[0.8949628,0.06095285,0.006384163,0.003687666,0.0332011,0.0008114525],"domain_scores_gemma":[0.6827347,0.2329387,0.01351862,0.01763046,0.0517207,0.001456796],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001434214,0.0008076343,0.04875066,0.004282353,0.0009095899,0.0004291222,0.001059496,0.1055317,0.01074971,0.1515267,0.008340796,0.666178],"study_design_scores_gemma":[0.0002804705,0.001495334,0.01142664,0.001816797,0.0003026859,0.0007847648,0.001095002,0.8089262,0.02411246,0.128151,0.0214235,0.0001850702],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07202115,0.02551408,0.8759357,0.00566112,0.0006957893,0.0009852048,0.0004521199,0.001866702,0.01686812],"genre_scores_gemma":[0.5557848,0.005924137,0.4350782,0.0004956691,0.0003367687,0.0008999196,0.0005037449,0.0001484109,0.0008283173],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9102846,"threshold_uncertainty_score":0.4744658,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2093331366","doi":"10.1109/cns.2014.6997492","title":"Towards effective feature selection in machine learning-based botnet detection approaches","year":2014,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":276,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Botnet; Computer science; Artificial intelligence; Host (biology); Computer security; Network security; Feature selection; Machine learning; Data mining; The Internet; World Wide Web","authors":[{"name":"Elaheh Biglar Beigi","is_ca":true},{"name":"Hossein Hadian Jazi","is_ca":true},{"name":"Natalia Stakhanova","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009767839187464834,"gpt":0.2032971031364725,"spread":0.1935292639490076,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003548005,0.001812419,0.00219695,0.00341669,0.0005332454,0.001303622,0.001583764,0.001430883,0.000988748],"category_scores_gemma":[0.007498754,0.0004936462,0.001039959,0.002288452,0.0005652134,0.001506921,0.001083306,0.001455911,0.0007247633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005065328,"about_ca_system_score_gemma":0.0007546275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001462459,"about_ca_topic_score_gemma":0.001199345,"domain_scores_codex":[0.9980488,0.0007898916,0.0001785624,0.0003798111,0.0004468294,0.000156087],"domain_scores_gemma":[0.9945593,0.0037232,0.0003315342,0.0003367272,0.0009461444,0.0001031144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004090282,0.0008939623,0.008898722,0.0004943772,0.0002469396,0.0003220022,0.0001861734,0.1192598,0.02252081,0.003121946,0.004696773,0.8389495],"study_design_scores_gemma":[0.00005853307,0.0002190314,0.002060799,0.00004641289,0.00007335205,0.0001521675,0.0000809893,0.9812838,0.00870053,0.005454223,0.001848455,0.00002168559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05362659,0.001432365,0.9414486,0.0003861831,0.00006651732,0.0002109035,0.000273373,0.001992656,0.000562867],"genre_scores_gemma":[0.435322,0.0007170273,0.5599395,0.0003435607,0.0002106475,0.0005195806,0.001611045,0.000178285,0.0011584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003548005,"threshold_uncertainty_score":0.0187639,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2915969651","doi":"10.1109/lnet.2019.2901792","title":"On the Feasibility of Deep Learning in Sensor Network Intrusion Detection","year":2019,"lang":"en","type":"article","venue":"IEEE Networking Letters","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":274,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intrusion detection system; Computer science; Artificial intelligence; Deep learning; Boltzmann machine; Machine learning; Wireless sensor network; Computer network","authors":[{"name":"Safa Otoum","is_ca":true},{"name":"Burak Kantarcı","is_ca":true},{"name":"Hussein T. Mouftah","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01680206702167992,"gpt":0.2265132101709739,"spread":0.209711143149294,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002718105,0.0007620899,0.0006159815,0.000487278,0.0002893326,0.0009413155,0.0009881251,0.001002851,0.001123695],"category_scores_gemma":[0.01020688,0.0003619851,0.0003689739,0.0005655998,0.001147748,0.00223965,0.0009836864,0.001791982,0.0002282497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009187856,"about_ca_system_score_gemma":0.0007421202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002210257,"about_ca_topic_score_gemma":0.001632826,"domain_scores_codex":[0.9991837,0.0003425167,0.00003077858,0.0001520016,0.0002139085,0.00007713775],"domain_scores_gemma":[0.9956518,0.003305372,0.0001741314,0.0003118186,0.0004845895,0.00007227793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001974402,0.0001229188,0.003173475,0.000155905,0.0001062373,0.00007036671,0.00008012057,0.7505645,0.003716961,0.08589037,0.001611925,0.1543099],"study_design_scores_gemma":[0.000003747919,0.00003247507,0.000133557,0.000007539876,0.000005772034,0.00001189896,0.000005271962,0.9872951,0.0008013083,0.01126549,0.000434403,0.000003360946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0523648,0.002253456,0.9365444,0.001874629,0.000120504,0.00004550865,0.00006255184,0.0005190668,0.006215035],"genre_scores_gemma":[0.8872441,0.001531556,0.1074153,0.0005721208,0.0001432124,0.00007176519,0.00008093902,0.00005159414,0.002889466],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002718105,"threshold_uncertainty_score":0.01437491,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3048313003","doi":"10.1109/tnsm.2020.3014929","title":"Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":274,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Intrusion detection system; Computer science; Oversampling; Artificial intelligence; Machine learning; Constant false alarm rate; Feature selection; Sample size determination; Network security; Data mining; Feature (linguistics); Dependency (UML); Mathematics; Statistics; Computer security","authors":[{"name":"MohammadNoor Injadat","is_ca":true},{"name":"Abdallah Moubayed","is_ca":true},{"name":"Ali Bou Nassif","is_ca":true},{"name":"Abdallah Shami","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02953981615032476,"gpt":0.2499424353563529,"spread":0.2204026192060281,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002025382,0.001161803,0.001479696,0.0008838565,0.0003350967,0.001048742,0.001959237,0.001031577,0.001494684],"category_scores_gemma":[0.003228491,0.0008068102,0.001083774,0.0007822534,0.0006583761,0.00113512,0.001075372,0.001766683,0.0005023561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001169977,"about_ca_system_score_gemma":0.001668429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007383768,"about_ca_topic_score_gemma":0.007794261,"domain_scores_codex":[0.9990628,0.0003638471,0.00005542303,0.0002154629,0.0001893427,0.0001131224],"domain_scores_gemma":[0.9989416,0.0005767103,0.000113488,0.00007593453,0.0002505425,0.00004175302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009233623,0.00006814332,0.001136252,0.00004487637,0.00008517477,0.00004673436,0.00002772426,0.9421392,0.001120903,0.003282121,0.0007025378,0.05125391],"study_design_scores_gemma":[0.000002264683,0.00001045671,0.00006132582,0.000001194616,0.000003757231,0.000004155153,0.000001065354,0.9991418,0.0001576882,0.0005334379,0.00008110099,0.000001770113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009515161,0.0003132421,0.988636,0.0001435586,0.00001950261,0.00005326494,0.0000579339,0.0008248333,0.0004364544],"genre_scores_gemma":[0.6087809,0.0003860683,0.3848016,0.0002964369,0.00009461796,0.0004721402,0.0005156926,0.0002002692,0.004452173],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007383768,"threshold_uncertainty_score":0.01468158,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2908941882","doi":"10.1109/tmm.2019.2893549","title":"Hybrid Deep-Learning-Based Anomaly Detection Scheme for Suspicious Flow Detection in SDN: A Social Multimedia Perspective","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":269,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Fundação para a Ciência e a Tecnologia; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer science; Anomaly detection; Quality of service; Scalability; Multimedia; Computer network; Machine learning; Artificial intelligence; Database","authors":[{"name":"Sahil Garg","is_ca":true},{"name":"Kuljeet Kaur","is_ca":true},{"name":"Neeraj Kumar","is_ca":false},{"name":"Joel J. P. C. Rodrigues","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008978700472931145,"gpt":0.235699129488738,"spread":0.2267204290158069,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001391763,0.0007913389,0.0009533632,0.0008516349,0.0005092769,0.0006780812,0.001878551,0.0009626467,0.000660738],"category_scores_gemma":[0.003102633,0.0002374863,0.0004851151,0.0007004521,0.0006168169,0.001678252,0.001297115,0.001381656,0.0001962667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001264913,"about_ca_system_score_gemma":0.001339716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006957082,"about_ca_topic_score_gemma":0.006653585,"domain_scores_codex":[0.9992175,0.0001513823,0.0000490771,0.0001780445,0.0002469726,0.0001570779],"domain_scores_gemma":[0.9987962,0.0003309986,0.0001391765,0.0001283999,0.0004870926,0.0001180608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006396474,0.000583623,0.013033,0.00009759873,0.000179468,0.0003171642,0.0002005041,0.4569166,0.01690124,0.007581653,0.005198831,0.4983507],"study_design_scores_gemma":[0.000002993892,0.00002200717,0.0002313849,0.00000169315,0.000005332713,0.00001505763,0.000006541944,0.9973531,0.001302354,0.0008976339,0.0001581292,0.000003797857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1390895,0.0005986798,0.8556088,0.0007206517,0.0001289642,0.00008355175,0.0001476582,0.002116549,0.001505641],"genre_scores_gemma":[0.9017016,0.000200426,0.09517228,0.0002823605,0.00005915177,0.00006702,0.0003246973,0.00005560829,0.002136882],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006957082,"threshold_uncertainty_score":0.01383317,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4292506131","doi":"10.3390/s22165986","title":"A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":256,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Overfitting; Computer science; Particle swarm optimization; Intrusion detection system; Random forest; Support vector machine; Data mining; Artificial intelligence; Benchmark (surveying); Machine learning; Fitness function; Genetic algorithm; Artificial neural network","authors":[{"name":"Amit Kumar Balyan","is_ca":false},{"name":"Sachin Ahuja","is_ca":false},{"name":"Umesh Kumar Lilhore","is_ca":false},{"name":"Sanjeev Kumar Sharma","is_ca":false},{"name":"Poongodi Manoharan","is_ca":false},{"name":"Abeer D. Algarni","is_ca":false},{"name":"Hela Elmannai","is_ca":false},{"name":"Kaamran Raahemifar","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01504466501042972,"gpt":0.2472517556703996,"spread":0.2322070906599699,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008446501,0.0008602906,0.00137367,0.00111607,0.0004106704,0.0009278565,0.001641413,0.001038601,0.001315554],"category_scores_gemma":[0.00125534,0.0004399908,0.001459787,0.0009496685,0.0003391067,0.001092681,0.0004747855,0.0007521764,0.0003373773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005557026,"about_ca_system_score_gemma":0.0009621699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01395549,"about_ca_topic_score_gemma":0.008144442,"domain_scores_codex":[0.9994718,0.0001135089,0.00003234475,0.0001503206,0.0001457994,0.00008617315],"domain_scores_gemma":[0.9995406,0.0002007218,0.00005866062,0.00002403429,0.000154778,0.00002131742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006794809,0.00005837744,0.002156099,0.00005426539,0.00007429376,0.00009731564,0.00003289448,0.9293695,0.001271412,0.002251805,0.001155415,0.06341068],"study_design_scores_gemma":[0.000003780061,0.00001070156,0.0001118886,0.0000022982,0.000006179304,0.00001582003,0.00000191345,0.9992286,0.0001023518,0.0003647695,0.0001490433,0.000002677692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03795447,0.000842533,0.9569662,0.0002839873,0.000105017,0.00006837882,0.0001387624,0.0007147519,0.002925883],"genre_scores_gemma":[0.7702174,0.0008181033,0.2216859,0.0002178615,0.00009759141,0.0002721501,0.0005495184,0.0000782092,0.006063236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01395549,"threshold_uncertainty_score":0.02774853,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2153919695","doi":"10.1109/icc.2006.255127","title":"Anomaly Based Network Intrusion Detection with Unsupervised Outlier Detection","year":2006,"lang":"en","type":"article","venue":"2006 IEEE International Conference on Communications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":247,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Anomaly detection; Outlier; Computer science; Anomaly (physics); Intrusion detection system; Data mining; Random forest; Artificial intelligence; Anomaly-based intrusion detection system; Pattern recognition (psychology); Machine learning","authors":[{"name":"Jiong Zhang","is_ca":true},{"name":"Mohammad Zulkernine","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0376647273698407,"gpt":0.2678853771644259,"spread":0.2302206497945852,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002051475,0.0009651667,0.001711281,0.00349976,0.0005586713,0.001244987,0.001834824,0.00105228,0.0004880567],"category_scores_gemma":[0.007822873,0.0003880497,0.00112517,0.003058861,0.0007253084,0.002071868,0.001555179,0.001245094,0.0004202378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005695085,"about_ca_system_score_gemma":0.0008350132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00164783,"about_ca_topic_score_gemma":0.002064639,"domain_scores_codex":[0.9960814,0.0008660713,0.0003227611,0.000793588,0.001686192,0.0002500452],"domain_scores_gemma":[0.9942965,0.001944462,0.001008211,0.001061131,0.001548356,0.0001413946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004834411,0.0007384068,0.03406646,0.0002641081,0.0005804725,0.0004952576,0.0002224949,0.1819373,0.02500642,0.007480446,0.007069998,0.7416552],"study_design_scores_gemma":[0.0000219885,0.0001229711,0.004263192,0.00001193174,0.00003866163,0.0005299633,0.0000401174,0.9731232,0.01243059,0.006935985,0.00244694,0.00003441344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03101785,0.0001809285,0.963796,0.0001220492,0.00004999587,0.0001353843,0.0002231181,0.003902522,0.0005722256],"genre_scores_gemma":[0.4343913,0.0002023483,0.5623965,0.0001161515,0.0001032033,0.0002738424,0.001332128,0.0001383253,0.001046162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00349976,"threshold_uncertainty_score":0.01084936,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285106051","doi":"10.1109/access.2022.3176317","title":"Design and Development of RNN Anomaly Detection Model for IoT Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":247,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Deep learning; Artificial intelligence; Recurrent neural network; Anomaly detection; Convolutional neural network; Machine learning; Intrusion detection system; Artificial neural network; Data mining","authors":[{"name":"Imtiaz Ullah","is_ca":true},{"name":"Qusay H. Mahmoud","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05390829886113963,"gpt":0.2737549412225625,"spread":0.2198466423614229,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000490765,0.0007296149,0.0005460232,0.0004850125,0.0003813003,0.0005908067,0.001608958,0.0007239112,0.001916462],"category_scores_gemma":[0.0006878813,0.0003797343,0.0006225342,0.0003588726,0.0003154613,0.0009120191,0.0005170454,0.001089573,0.0007477668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012111,"about_ca_system_score_gemma":0.001076335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01479855,"about_ca_topic_score_gemma":0.009085217,"domain_scores_codex":[0.9997273,0.00003068821,0.00002107796,0.00009991686,0.00008595392,0.00003501115],"domain_scores_gemma":[0.9997683,0.00003662404,0.00002593991,0.00001652925,0.0001400841,0.00001257105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000881743,0.00005661098,0.001571908,0.00007563484,0.00004813671,0.0001266794,0.00004963822,0.858192,0.01170096,0.006461532,0.001693461,0.1199353],"study_design_scores_gemma":[0.0000015897,0.000009865012,0.00005937885,0.000001887997,0.000003631645,0.00001000039,0.000001477462,0.9981903,0.0009553084,0.0004641517,0.0002998347,0.000002517295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009968404,0.0002025689,0.986096,0.0001489081,0.00005618015,0.00005087322,0.0000951859,0.001395671,0.001986138],"genre_scores_gemma":[0.7344968,0.0006094714,0.2552328,0.0002118236,0.00006715675,0.0003995981,0.0005423496,0.0001694899,0.008270547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01479855,"threshold_uncertainty_score":0.02942485,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2904959125","doi":"10.1109/comst.2018.2885894","title":"Routing Attacks and Mitigation Methods for RPL-Based Internet of Things","year":2018,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":239,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Routing protocol; Internet of Things; Intrusion detection system; Routing (electronic design automation); Computer security; The Internet; Protocol (science); Computer network; World Wide Web","authors":[{"name":"Ahmed Raoof","is_ca":true},{"name":"Ashraf Matrawy","is_ca":true},{"name":"Chung–Horng Lung","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06576104382010177,"gpt":0.3783829431120326,"spread":0.3126218992919308,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002465737,0.001378516,0.0009213981,0.002996424,0.0009114541,0.001757977,0.001817259,0.001439407,0.0008608945],"category_scores_gemma":[0.005192038,0.0004841406,0.001386167,0.001418842,0.001140615,0.003432699,0.001313255,0.001795909,0.0006644438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007515853,"about_ca_system_score_gemma":0.0006498673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002350858,"about_ca_topic_score_gemma":0.0002305443,"domain_scores_codex":[0.9959987,0.0009879796,0.0004581497,0.0005662665,0.001753801,0.0002350267],"domain_scores_gemma":[0.9968145,0.00110819,0.0005101071,0.0007361493,0.0007739993,0.00005693148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001771828,0.000238821,0.003348329,0.002630813,0.0003101696,0.0005121364,0.0005735141,0.02305949,0.02721385,0.1103798,0.009974871,0.8215811],"study_design_scores_gemma":[0.00006088145,0.001164898,0.004528915,0.002022347,0.0009262968,0.009773446,0.0007333116,0.4497766,0.1054987,0.0999002,0.3252205,0.000393903],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01411501,0.03909997,0.9255642,0.001757174,0.001261453,0.0005035332,0.00008503609,0.001724308,0.01588931],"genre_scores_gemma":[0.4681446,0.04660569,0.472365,0.001532083,0.00170764,0.000748802,0.000564774,0.0002435869,0.008087819],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.002996424,"threshold_uncertainty_score":0.01304024,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2105742458","doi":"10.1201/1079/43253.27.7.20000101/30304.4","title":"Network Intrusion Detection: An Analyst's Hand-book","year":2000,"lang":"en","type":"article","venue":"EDPACS","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":234,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec","funders":"","keywords":"Intrusion detection system; Computer science; Intrusion; Computer security; Geology","authors":[{"name":"Stephen Northcutt","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0098457379748076,"gpt":0.2208104382850029,"spread":0.2109647003101953,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008962828,0.001208109,0.001001293,0.003143759,0.001383108,0.00473549,0.001005916,0.001639979,0.05526165],"category_scores_gemma":[0.005765049,0.001128772,0.0004669506,0.003571089,0.0007720992,0.006202959,0.001910971,0.003100606,0.04222293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001095833,"about_ca_system_score_gemma":0.001829011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002196028,"about_ca_topic_score_gemma":0.004512926,"domain_scores_codex":[0.9981362,0.0001437132,0.00009124797,0.0001839948,0.00139059,0.00005421143],"domain_scores_gemma":[0.9969347,0.001029084,0.0001085496,0.0002642912,0.001433652,0.0002298128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001184486,0.00001770826,0.00009667141,0.0001237632,0.000005816331,0.00005070553,0.0001266048,0.0001980384,0.000586662,0.005805196,0.8396156,0.1533614],"study_design_scores_gemma":[0.000001587005,0.000009531132,0.0001613899,0.00009737175,0.00000341739,0.0002402667,0.00005102419,0.0003894283,0.0001831199,0.002529016,0.996326,0.000007762898],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002628031,0.1105967,0.1116801,0.04342625,0.0374524,0.0004387941,0.002893873,0.01562351,0.6752604],"genre_scores_gemma":[0.007548094,0.04049825,0.04056301,0.009631142,0.004909222,0.0001544553,0.002404221,0.002962432,0.8913292],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.05526165,"threshold_uncertainty_score":0.1848686,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1492196703","doi":"10.6633/ijns.200509.1(2).05","title":"RESEARCH ON INTRUSION DETECTION AND RESPONSE: A SURVEY","year":2005,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":220,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Dependability; Intrusion detection system; Computer science; Intrusion; Computer security; Port (circuit theory); Intrusion prevention system; Network security; Data science; Software engineering; Electrical engineering","authors":[{"name":"Peyman Kabiri","is_ca":false},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06001173397870684,"gpt":0.3366074659293021,"spread":0.2765957319505952,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00363893,0.001311337,0.002471496,0.006943068,0.0006203097,0.00301356,0.001669815,0.00166619,0.003939306],"category_scores_gemma":[0.008615348,0.0007349877,0.0009405848,0.0112053,0.0007046799,0.007152592,0.0007116236,0.001181014,0.003321104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008774444,"about_ca_system_score_gemma":0.00111809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001235596,"about_ca_topic_score_gemma":0.001104259,"domain_scores_codex":[0.9963009,0.0007491357,0.0004995328,0.0006465768,0.001618559,0.0001852526],"domain_scores_gemma":[0.9878837,0.008059865,0.0005144182,0.0004676458,0.002829079,0.0002452552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001298453,0.0001927151,0.004585391,0.004288032,0.0001016846,0.0001284967,0.000197536,0.001654604,0.001697528,0.003747009,0.01218707,0.9710901],"study_design_scores_gemma":[0.00009571699,0.00131212,0.0139331,0.007216593,0.000537693,0.004846577,0.001758335,0.01593865,0.009507593,0.01516032,0.929502,0.0001912925],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01324312,0.9086696,0.04592399,0.003727966,0.001261576,0.000230731,0.0003921925,0.0006674817,0.02588333],"genre_scores_gemma":[0.04293894,0.919364,0.02830574,0.001498454,0.00159407,0.000163713,0.0009233534,0.0001008404,0.005110841],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006943068,"threshold_uncertainty_score":0.01924467,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1994373811","doi":"10.1155/2009/837601","title":"Network Anomaly Detection Based on Wavelet Analysis","year":2008,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":220,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Wavelet; Intrusion detection system; Anomaly detection; Data mining; Identification (biology); SIGNAL (programming language); Artificial intelligence; Pattern recognition (psychology); Machine learning","authors":[{"name":"Wei Lu","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0149755649260582,"gpt":0.2532259055209763,"spread":0.2382503405949181,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001107298,0.0008183339,0.001031572,0.002921287,0.0002708311,0.0008778174,0.0007939284,0.0006547378,0.000578517],"category_scores_gemma":[0.004145781,0.0002781479,0.0007405349,0.002466572,0.0004347681,0.001968786,0.0008295635,0.001140713,0.0005403797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003373086,"about_ca_system_score_gemma":0.0003491679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009971999,"about_ca_topic_score_gemma":0.0007118555,"domain_scores_codex":[0.9990425,0.0001718745,0.00006110069,0.0001749944,0.0004699015,0.00007958135],"domain_scores_gemma":[0.9984837,0.0006572433,0.000225022,0.0002318734,0.0003582615,0.00004397194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003532965,0.0002207182,0.01280988,0.0002001955,0.0002181158,0.0003542544,0.0001862418,0.145189,0.08651406,0.01213913,0.002768843,0.7390464],"study_design_scores_gemma":[0.000007674314,0.00005870059,0.002370464,0.000008090061,0.00002205925,0.0001932769,0.00002332064,0.9802265,0.01139202,0.004471578,0.001209436,0.00001686255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03010846,0.0001369428,0.9683133,0.00007440631,0.00003127379,0.00002812695,0.00009123645,0.0007606588,0.0004556699],"genre_scores_gemma":[0.5285662,0.0007533859,0.4682495,0.00006504774,0.0001151023,0.00009723507,0.0007398746,0.0001525258,0.001260968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002921287,"threshold_uncertainty_score":0.005856037,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2597441556","doi":"10.1109/icissec.2016.7885840","title":"An Evaluation Framework for Intrusion Detection Dataset","year":2016,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":210,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Intrusion detection system; Benchmark (surveying); Variety (cybernetics); The Internet; Data mining; Network security; Intrusion prevention system; Computer security; Machine learning; Artificial intelligence; World Wide Web","authors":[{"name":"Amirhossein Gharib","is_ca":true},{"name":"Iman Sharafaldin","is_ca":true},{"name":"Arash Habibi Lashkari","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03187034655257093,"gpt":0.3136703403361588,"spread":0.2817999937835878,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02971988,0.002752266,0.001977762,0.01149501,0.002123532,0.004887964,0.006020898,0.002864794,0.002760438],"category_scores_gemma":[0.05825573,0.0005696439,0.002472613,0.01062108,0.001126859,0.004633293,0.003693266,0.003274545,0.002220726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00529937,"about_ca_system_score_gemma":0.004722879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01193062,"about_ca_topic_score_gemma":0.01552741,"domain_scores_codex":[0.9678271,0.009948771,0.006756718,0.003953237,0.01041252,0.001101536],"domain_scores_gemma":[0.9589496,0.01159128,0.003538806,0.009284335,0.01531715,0.001318873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00210171,0.004304675,0.06534753,0.004980816,0.002476363,0.0003661448,0.0004903102,0.06644565,0.008699588,0.03017446,0.4940416,0.3205712],"study_design_scores_gemma":[0.001375681,0.002912143,0.08757783,0.001255302,0.0009780076,0.001594645,0.001299807,0.4522152,0.0266772,0.0297701,0.3938845,0.0004596329],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.09364782,0.005243926,0.2212265,0.004999957,0.001079764,0.0142345,0.6118991,0.03017827,0.01749018],"genre_scores_gemma":[0.09008075,0.0005393579,0.2230159,0.0006912149,0.0001620289,0.008478492,0.674532,0.0005619614,0.001938348],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02971988,"threshold_uncertainty_score":0.1571757,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4293812121","doi":"10.1109/jiot.2022.3203249","title":"A Survey on IoT Intrusion Detection: Federated Learning, Game Theory, Social Psychology, and Explainable AI as Future Directions","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":198,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Cégep de l'Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Data science; Intrusion detection system; Game theory; Internet of Things; Intrusion; Artificial intelligence; World Wide Web; Mathematical economics","authors":[{"name":"Sarhad Arisdakessian","is_ca":true},{"name":"Omar Abdel Wahab","is_ca":true},{"name":"Azzam Mourad","is_ca":false},{"name":"Hadi Otrok","is_ca":false},{"name":"Mohsen Guizani","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01401494987122598,"gpt":0.274704485661494,"spread":0.260689535790268,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003519543,0.001409762,0.001784093,0.004928325,0.000797523,0.004337317,0.002292616,0.002245458,0.003421265],"category_scores_gemma":[0.007319594,0.0006816013,0.00121963,0.006406388,0.002190189,0.008120675,0.001761621,0.003229872,0.0007996865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002009925,"about_ca_system_score_gemma":0.001707866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002628164,"about_ca_topic_score_gemma":0.002234714,"domain_scores_codex":[0.9981481,0.0007182273,0.0001698451,0.0002767917,0.0005704687,0.0001167158],"domain_scores_gemma":[0.9915838,0.006826588,0.0002732899,0.0003438435,0.0008046099,0.0001678438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005843198,0.0003264526,0.004242763,0.004632288,0.0001817584,0.0002077735,0.0006720982,0.009721693,0.0004463118,0.2244884,0.02135986,0.7336622],"study_design_scores_gemma":[0.00003464468,0.0003071244,0.005497701,0.007031145,0.0001838573,0.0009988017,0.002215053,0.0878294,0.001050879,0.5411988,0.3534937,0.0001588337],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.006592899,0.814002,0.1254736,0.01791581,0.001182199,0.0001657649,0.0001744243,0.0002943336,0.03419894],"genre_scores_gemma":[0.08490972,0.8529396,0.05044997,0.003273726,0.002980102,0.0002455221,0.0003461237,0.00007453744,0.004780714],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004928325,"threshold_uncertainty_score":0.0186134,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2111890927","doi":"10.1109/ares.2006.7","title":"A hybrid network intrusion detection technique using random forests","year":2006,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":195,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"Air Force Research Laboratory","keywords":"Intrusion detection system; Misuse detection; Anomaly detection; Anomaly-based intrusion detection system; Computer science; Anomaly (physics); Data mining; Network security; Artificial intelligence; False positive rate; Pattern recognition (psychology); Computer security","authors":[{"name":"J. Zhang","is_ca":true},{"name":"Mohammad Zulkernine","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008564487096383336,"gpt":0.2180265338015144,"spread":0.2094620467051311,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003108122,0.001195299,0.001653948,0.003236052,0.0006498735,0.0007813691,0.001805974,0.00118738,0.0009027094],"category_scores_gemma":[0.003946739,0.0005266472,0.001651175,0.002275925,0.0003539984,0.001866468,0.000932751,0.0009440847,0.0007301279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003931541,"about_ca_system_score_gemma":0.0006110097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002894914,"about_ca_topic_score_gemma":0.004920365,"domain_scores_codex":[0.9978655,0.0005563446,0.0001265301,0.000448945,0.0008403562,0.0001623819],"domain_scores_gemma":[0.9978642,0.0009550527,0.0002434849,0.0002754594,0.0005721533,0.00008970303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003614635,0.0003520052,0.008446638,0.000205645,0.0006720216,0.0003635613,0.0001366564,0.1528765,0.02469074,0.003588618,0.008149171,0.800157],"study_design_scores_gemma":[0.00002839857,0.000139136,0.001905108,0.00001587849,0.00009897037,0.000462039,0.00001935466,0.9817361,0.008207946,0.004247948,0.003090189,0.0000489037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01589884,0.0003487321,0.9785049,0.0001266594,0.00007794552,0.0001102976,0.0001813837,0.004295998,0.0004552559],"genre_scores_gemma":[0.2365928,0.0002727798,0.7603551,0.0001676305,0.0001241116,0.0002042014,0.0006417527,0.0001872512,0.001454399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003236052,"threshold_uncertainty_score":0.01643753,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3161325351","doi":"10.1109/mce.2021.3081874","title":"Secure and Resilient Artificial Intelligence of Things: A HoneyNet Approach for Threat Detection and Situational Awareness","year":2021,"lang":"en","type":"article","venue":"IEEE Consumer Electronics Magazine","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":181,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brandon University","funders":"Japan Society for the Promotion of Science; National Natural Science Foundation of China","keywords":"Honeypot; Situation awareness; Computer science; Computer security; Resilience (materials science); Software deployment; Cloud computing; Artificial intelligence; Engineering; Software engineering","authors":[{"name":"Liang Tan","is_ca":false},{"name":"Keping Yu","is_ca":false},{"name":"Fangpeng Ming","is_ca":false},{"name":"Xiaofan Cheng","is_ca":false},{"name":"Gautam Srivastava","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02097017967139755,"gpt":0.2554255969756928,"spread":0.2344554173042952,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007078781,0.0005534859,0.0005235463,0.0005493059,0.000569066,0.001248123,0.0009002438,0.000760916,0.0006288391],"category_scores_gemma":[0.0009941913,0.0003176405,0.0004588711,0.0003068957,0.001088599,0.002938398,0.001481976,0.001062677,0.0001421637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004816387,"about_ca_system_score_gemma":0.0004837912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007689103,"about_ca_topic_score_gemma":0.001464641,"domain_scores_codex":[0.9995556,0.0001402892,0.00003237848,0.00009554858,0.0001222266,0.00005383118],"domain_scores_gemma":[0.9995004,0.0001481854,0.00006489485,0.0001369653,0.00009604476,0.00005347184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003328309,0.0004071959,0.007490962,0.0004437428,0.0004141816,0.0008597628,0.001094622,0.4023873,0.08840268,0.1244873,0.01031991,0.3633594],"study_design_scores_gemma":[0.00001489837,0.0001607476,0.0009619078,0.00002656512,0.00004247953,0.0002162683,0.0001690842,0.9501232,0.009286787,0.02972881,0.009237663,0.00003169801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05319428,0.001115635,0.9363263,0.001603857,0.0002587415,0.0001941836,0.00004312622,0.001087086,0.006176836],"genre_scores_gemma":[0.8056867,0.0008257715,0.1900056,0.0004602896,0.0000829908,0.0001555127,0.00007708279,0.00005279388,0.002653315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001248123,"threshold_uncertainty_score":0.003743649,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3135091917","doi":"10.1007/s10922-021-09589-6","title":"AS-IDS: Anomaly and Signature Based IDS for the Internet of Things","year":2021,"lang":"en","type":"article","venue":"Journal of Network and Systems Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":181,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Network packet; Intrusion detection system; Data mining; Cluster analysis; Computer network; Preprocessor; Anomaly detection; Signature (topology); Artificial intelligence","authors":[{"name":"Yazan Otoum","is_ca":true},{"name":"Amiya Nayak","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01052702281262555,"gpt":0.2193439191049446,"spread":0.208816896292319,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008344225,0.0006410222,0.0008687485,0.001059904,0.0005605521,0.001193327,0.001242546,0.0007486363,0.003499108],"category_scores_gemma":[0.001429564,0.0002330725,0.0003089987,0.0006881928,0.0004611159,0.00164843,0.001074219,0.000985719,0.001645601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005741948,"about_ca_system_score_gemma":0.0009214886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008092221,"about_ca_topic_score_gemma":0.001033608,"domain_scores_codex":[0.9991621,0.0001231214,0.00006474742,0.0001284089,0.0004338903,0.00008764939],"domain_scores_gemma":[0.9991094,0.0001352669,0.00008435195,0.0002390192,0.0003312824,0.0001006669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002407019,0.0005510239,0.007135064,0.0006238877,0.0002415185,0.0006010436,0.0002308754,0.03245743,0.06998775,0.05870513,0.183989,0.6430703],"study_design_scores_gemma":[0.0002172653,0.0009086626,0.002637213,0.00006229969,0.0001336244,0.001519964,0.0001254274,0.7387993,0.08844739,0.03556771,0.1314568,0.000124336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03595204,0.001050625,0.8792968,0.0009236131,0.002394202,0.0003401552,0.002043843,0.06692211,0.01107645],"genre_scores_gemma":[0.5155991,0.0008272313,0.4476849,0.0008423468,0.000517079,0.0003067304,0.005218084,0.001039395,0.02796503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003499108,"threshold_uncertainty_score":0.0117057,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4362672268","doi":"10.1016/j.iot.2023.100780","title":"Internet of Things (IoT) security dataset evolution: Challenges and future directions","year":2023,"lang":"en","type":"article","venue":"Internet of Things","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":175,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada; University of New Brunswick","funders":"","keywords":"Computer science; Intrusion detection system; Internet of Things; Computer security; Key (lock); Protocol (science); Automation; Artificial intelligence; Engineering","authors":[{"name":"Barjinder Kaur","is_ca":true},{"name":"Sajjad Dadkhah","is_ca":true},{"name":"Farzaneh Shoeleh","is_ca":true},{"name":"Euclides Carlos Pinto Neto","is_ca":true},{"name":"Pulei Xiong","is_ca":true},{"name":"Shahrear Iqbal","is_ca":true},{"name":"Philippe Lamontagne","is_ca":true},{"name":"Suprio Ray","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01950765477611266,"gpt":0.2426392595627827,"spread":0.22313160478667,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01321791,0.001066172,0.001542428,0.004014268,0.001609007,0.005103054,0.003698626,0.001898073,0.001894912],"category_scores_gemma":[0.02633344,0.0003976164,0.001431445,0.00583345,0.001004438,0.008910217,0.003055503,0.004468406,0.002094294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002397768,"about_ca_system_score_gemma":0.003926082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01311274,"about_ca_topic_score_gemma":0.02404479,"domain_scores_codex":[0.9911211,0.00203994,0.0009565446,0.001669279,0.003527369,0.0006857372],"domain_scores_gemma":[0.9659413,0.006269353,0.0018176,0.01030816,0.01312636,0.002537198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008365567,0.001253052,0.1271067,0.001774737,0.0005700622,0.0002292106,0.0005928936,0.01344775,0.01882437,0.01578383,0.3975476,0.4220333],"study_design_scores_gemma":[0.0002209949,0.0006049822,0.1261685,0.001076926,0.0003972228,0.001349276,0.003366437,0.1588253,0.01846173,0.03171139,0.6575569,0.0002604169],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.3152406,0.02444435,0.1218268,0.1589122,0.0120906,0.002049229,0.3152062,0.02061816,0.02961176],"genre_scores_gemma":[0.2183791,0.005787865,0.1708842,0.0113289,0.001481728,0.0007887888,0.5844625,0.0009543954,0.005932541],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01321791,"threshold_uncertainty_score":0.06990385,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4293093536","doi":"10.1109/pst55820.2022.9851966","title":"Towards the Development of a Realistic Multidimensional IoT Profiling Dataset","year":2022,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":175,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Profiling (computer programming); Internet of Things; Transferability; Denial-of-service attack; Cloud computing; Computer security; Identification (biology); Mobile device; Intrusion detection system; Embedded system; Computer network; The Internet; Machine learning; World Wide Web","authors":[{"name":"Sajjad Dadkhah","is_ca":true},{"name":"Hassan Mahdikhani","is_ca":true},{"name":"Priscilla Kyei Danso","is_ca":true},{"name":"Alireza Zohourian","is_ca":true},{"name":"Kevin Anh Truong","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03902308788887636,"gpt":0.265668463782057,"spread":0.2266453758931806,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004562695,0.001174134,0.0009965465,0.002462804,0.001016864,0.002288855,0.003728235,0.002323564,0.001037321],"category_scores_gemma":[0.01558164,0.0005885347,0.001608176,0.002639508,0.0007354621,0.003409317,0.002780456,0.003105235,0.001324785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001979522,"about_ca_system_score_gemma":0.001419041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006968754,"about_ca_topic_score_gemma":0.01031921,"domain_scores_codex":[0.9959596,0.001184531,0.0004847747,0.0009948508,0.001062413,0.000313894],"domain_scores_gemma":[0.9921566,0.002046784,0.0007202293,0.002493554,0.002249071,0.000333778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009179674,0.004710491,0.1050583,0.001772911,0.0007008641,0.001280753,0.0008257221,0.2726579,0.02358618,0.0151777,0.2259483,0.3473628],"study_design_scores_gemma":[0.0001197952,0.0004895384,0.04610014,0.0003619016,0.0001042219,0.0008431872,0.0007385123,0.8469602,0.01418754,0.009351928,0.08057622,0.0001667681],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.3338543,0.0021487,0.4521788,0.006118246,0.001464023,0.002916388,0.1731684,0.01796468,0.01018649],"genre_scores_gemma":[0.3021114,0.0006032044,0.3660046,0.0009678577,0.0001665012,0.001851904,0.3263063,0.0003369452,0.001651378],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.006968754,"threshold_uncertainty_score":0.02413011,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1997741525","doi":"10.1016/j.patrec.2004.09.045","title":"Intrusion detection using hierarchical neural networks","year":2004,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":174,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Intrusion detection system; Anomaly detection; Artificial intelligence; Artificial neural network; Classifier (UML); Data mining; Pattern recognition (psychology); Anomaly-based intrusion detection system; Machine learning","authors":[{"name":"Chunlin Zhang","is_ca":true},{"name":"Ju Jiang","is_ca":true},{"name":"Mohamed S. Kamel","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02217877674600792,"gpt":0.2300956895851966,"spread":0.2079169128391887,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009007032,0.000586131,0.000656994,0.001348141,0.0004880232,0.0007521083,0.0009516199,0.000641303,0.001398049],"category_scores_gemma":[0.002955447,0.000435488,0.00060517,0.0007976764,0.000464346,0.001277218,0.0007265115,0.0007485243,0.0003643168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009584228,"about_ca_system_score_gemma":0.0006527195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008503464,"about_ca_topic_score_gemma":0.01124335,"domain_scores_codex":[0.9994372,0.0001375138,0.00003542884,0.0001255647,0.0001635856,0.0001007556],"domain_scores_gemma":[0.9985342,0.0007550399,0.0001597219,0.0001788078,0.0003152248,0.00005698619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003536562,0.0001974523,0.008799369,0.000153614,0.0002375106,0.000168911,0.0001259703,0.4311444,0.01832214,0.007974849,0.003721436,0.5288007],"study_design_scores_gemma":[0.000005768626,0.00002597998,0.0008127371,0.000004861605,0.00002256001,0.00001968053,0.000008792557,0.9928393,0.002312002,0.00366239,0.0002798807,0.000005902582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1002085,0.001298695,0.8914657,0.0003048621,0.00009335246,0.0001142014,0.0001807192,0.002678325,0.003655612],"genre_scores_gemma":[0.7955731,0.0004047677,0.2001981,0.0001482614,0.00006768612,0.00007918246,0.0003773994,0.00006962218,0.003081877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008503464,"threshold_uncertainty_score":0.01690793,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3000429356","doi":"10.1109/tnsm.2020.2967721","title":"Analyzing Data Granularity Levels for Insider Threat Detection Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":173,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Insider threat; Insider; Computer science; Granularity; Computer security; Machine learning; Artificial intelligence; Set (abstract data type)","authors":[{"name":"Duc C. Le","is_ca":true},{"name":"A. Nur Zincir‐Heywood","is_ca":true},{"name":"Malcolm I. Heywood","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07647239340849372,"gpt":0.2690621962632918,"spread":0.1925898028547981,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002282282,0.000932532,0.0009642715,0.003107671,0.0007272665,0.001650855,0.0008224498,0.0009804117,0.0007114433],"category_scores_gemma":[0.01057287,0.0002960898,0.0005798109,0.0015431,0.0005962473,0.002318247,0.001531826,0.001335901,0.0005417887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000783888,"about_ca_system_score_gemma":0.0005695042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001764543,"about_ca_topic_score_gemma":0.00229432,"domain_scores_codex":[0.9970797,0.0005589603,0.0003477598,0.0007220244,0.001051625,0.0002398892],"domain_scores_gemma":[0.990694,0.004209898,0.001616858,0.001861771,0.001279309,0.0003382037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00163146,0.00149338,0.1484876,0.0004722161,0.000261326,0.0009150254,0.001062224,0.1445839,0.101776,0.002975818,0.005393504,0.5909475],"study_design_scores_gemma":[0.0000274226,0.0004753064,0.04613227,0.00005297406,0.00005000682,0.0003800509,0.0003772554,0.9020885,0.04231192,0.005566354,0.002469619,0.00006845933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6484735,0.0008929976,0.3381324,0.0007736236,0.0001644514,0.0004027867,0.001521173,0.006500836,0.003138285],"genre_scores_gemma":[0.9200889,0.000100411,0.07829531,0.00007395575,0.00003947445,0.00009334806,0.0009484888,0.00006232915,0.0002978178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003107671,"threshold_uncertainty_score":0.01207,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313216104","doi":"10.1016/j.iot.2022.100656","title":"Deep learning-enabled anomaly detection for IoT systems","year":2022,"lang":"en","type":"article","venue":"Internet of Things","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":167,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Université Laval; Brock University; Polytechnique Montréal; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Computer science; Internet of Things; Autoencoder; Deep learning; Artificial intelligence; Data mining; Classifier (UML); Machine learning; Computer security","authors":[{"name":"Adel Abusitta","is_ca":true},{"name":"Glaucio H.S. de Carvalho","is_ca":true},{"name":"Omar Abdel Wahab","is_ca":true},{"name":"Talal Halabi","is_ca":true},{"name":"Benjamin C. M. Fung","is_ca":true},{"name":"Saja Al Mamoori","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009052556107596619,"gpt":0.2110428888073364,"spread":0.2019903326997398,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006280141,0.0006102961,0.0005978212,0.000759423,0.000301706,0.0007067436,0.0008748715,0.0005944768,0.001031435],"category_scores_gemma":[0.00220932,0.0002982174,0.0003705038,0.0007650972,0.0003327254,0.001277625,0.0008910826,0.001538244,0.0002760563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007772485,"about_ca_system_score_gemma":0.0006887246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00434303,"about_ca_topic_score_gemma":0.005342721,"domain_scores_codex":[0.9995627,0.00006458267,0.00002872682,0.0001053078,0.0001461729,0.00009255071],"domain_scores_gemma":[0.9991267,0.0003539255,0.000118649,0.0001145112,0.0002427538,0.0000434447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003436563,0.0003383594,0.01359015,0.0001560695,0.000168773,0.0001975995,0.00007149854,0.500413,0.01811829,0.01129546,0.0063392,0.448968],"study_design_scores_gemma":[0.000001361328,0.00001219899,0.00042683,0.000002752755,0.000004059402,0.00001747177,0.000003206795,0.994696,0.0014839,0.003109673,0.0002403366,0.000002308316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1157754,0.0009270832,0.8770508,0.0006835123,0.0001921112,0.00003273869,0.0004050852,0.002672621,0.002260775],"genre_scores_gemma":[0.9427122,0.0002231062,0.05486806,0.00009501846,0.00005107607,0.00002195849,0.0003373754,0.00007513777,0.001615999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00434303,"threshold_uncertainty_score":0.008635521,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3098596104","doi":"10.1109/tii.2020.3038761","title":"A Novel Web Attack Detection System for Internet of Things via Ensemble Classification","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":167,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Internet of Things; The Internet; World Wide Web; Computer security","authors":[{"name":"Chaochao Luo","is_ca":false},{"name":"Zhiyuan Tan","is_ca":false},{"name":"Geyong Min","is_ca":false},{"name":"Jie Gan","is_ca":false},{"name":"Wei Shi","is_ca":true},{"name":"Zhihong Tian","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08741384871318456,"gpt":0.2579254514548813,"spread":0.1705116027416967,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000920346,0.0009828546,0.001082883,0.001407395,0.0005032821,0.00080274,0.001136872,0.0008676631,0.0008501005],"category_scores_gemma":[0.001564137,0.0002970718,0.000694328,0.0006852141,0.0001721728,0.00167015,0.001160787,0.001265583,0.0006056605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006062551,"about_ca_system_score_gemma":0.0005240214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003490383,"about_ca_topic_score_gemma":0.004321453,"domain_scores_codex":[0.9992571,0.00008655488,0.00006283595,0.000198276,0.000259509,0.000135636],"domain_scores_gemma":[0.9992952,0.0001236513,0.00008375804,0.0001300639,0.0003137225,0.00005347199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004643546,0.0009663215,0.02271175,0.00008280823,0.0002992055,0.0004117553,0.00008711081,0.05251278,0.02575107,0.001263011,0.01284193,0.882608],"study_design_scores_gemma":[0.00001499443,0.0001164269,0.002989357,0.000006920959,0.00005206843,0.0001329184,0.00001944455,0.9845251,0.01001177,0.0006125493,0.001500966,0.00001743111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2213402,0.0008975392,0.7566156,0.0008107218,0.0004472505,0.0003329492,0.0007409378,0.01375869,0.005056038],"genre_scores_gemma":[0.8524426,0.0002596122,0.1407219,0.0005405819,0.00011387,0.0001734415,0.001591745,0.00008321336,0.004072976],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003490383,"threshold_uncertainty_score":0.006940126,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4224331011","doi":"10.1109/tii.2022.3164770","title":"Dependable Intrusion Detection System for IoT: A Deep Transfer Learning Based Approach","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":164,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Dependability; Computer science; Intrusion detection system; Scalability; Deep learning; Transfer of learning; Artificial intelligence; Internet of Things; Machine learning; Computer security; Distributed computing; Software engineering","authors":[{"name":"Sk. Tanzir Mehedi","is_ca":false},{"name":"Ziaur Rahman","is_ca":false},{"name":"Kawsar Ahmed","is_ca":true},{"name":"Rafiqul Islam","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02974021910514137,"gpt":0.2186506623449203,"spread":0.1889104432397789,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008854678,0.0007591843,0.0007007131,0.0006255759,0.0003362292,0.0007417412,0.001339063,0.0008858268,0.001176404],"category_scores_gemma":[0.001509536,0.0002750876,0.0006724637,0.0004454133,0.0004297319,0.001599877,0.001006955,0.001526423,0.0003220006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009676568,"about_ca_system_score_gemma":0.0008099554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003525337,"about_ca_topic_score_gemma":0.002246137,"domain_scores_codex":[0.9996191,0.00007661304,0.0000252296,0.0001103111,0.0001060056,0.00006258111],"domain_scores_gemma":[0.9996157,0.0001323152,0.00004558879,0.00004213347,0.0001370479,0.0000271501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002692136,0.0004435258,0.004200891,0.00009379043,0.0001534455,0.0002309797,0.000136542,0.6058732,0.01278572,0.007397997,0.003471786,0.3649428],"study_design_scores_gemma":[0.000002108985,0.00003068103,0.0001670882,0.000002593316,0.000006922802,0.00001787645,0.000004674688,0.9972607,0.001157133,0.00112798,0.0002178389,0.000004266731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07071071,0.0007279614,0.9228966,0.0005520648,0.0001327627,0.0001064065,0.00009144856,0.001990851,0.00279116],"genre_scores_gemma":[0.9127005,0.0004397129,0.08238509,0.0002623304,0.00005552981,0.0001325007,0.0001975539,0.00005208579,0.003774706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003525337,"threshold_uncertainty_score":0.007020831,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4297329009","doi":"10.1016/j.dcan.2022.09.008","title":"An ensemble deep learning model for cyber threat hunting in industrial internet of things","year":2022,"lang":"en","type":"article","venue":"Digital Communications and Networks","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":163,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Anomaly detection; Artificial intelligence; Machine learning; Decision tree; Support vector machine; Deep learning; Industrial Internet; Big data; Data mining; Data stream mining; Intrusion detection system; Internet of Things; Computer security","authors":[{"name":"Abbas Yazdinejad","is_ca":true},{"name":"Mostafa Kazemi","is_ca":false},{"name":"Reza M. Parizi","is_ca":false},{"name":"Ali Dehghantanha","is_ca":true},{"name":"Hadis Karimipour","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03825089836866944,"gpt":0.2586740276122913,"spread":0.2204231292436219,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005625035,0.0006589944,0.0006310921,0.0004576109,0.0002566426,0.0005273432,0.0009843103,0.0008263974,0.0008363712],"category_scores_gemma":[0.001007796,0.0002981662,0.0006590842,0.0005376947,0.0002617632,0.000873171,0.00056734,0.001144143,0.0002058142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005490337,"about_ca_system_score_gemma":0.0005799308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01048239,"about_ca_topic_score_gemma":0.01086819,"domain_scores_codex":[0.9998255,0.00002507959,0.00001077116,0.00005580783,0.00004001252,0.00004277067],"domain_scores_gemma":[0.9997837,0.0000717998,0.00002880063,0.00001611731,0.00008616873,0.00001339622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009709509,0.00009419399,0.003290248,0.00004144763,0.00007971012,0.0001130948,0.00005127484,0.902313,0.002289076,0.002357245,0.001709078,0.08756453],"study_design_scores_gemma":[8.721923e-7,0.000009768654,0.0001257334,0.000001756162,0.000004205178,0.000004975138,0.000001893431,0.9992454,0.0001302103,0.0004059966,0.00006789889,0.000001355212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2746544,0.001884706,0.714729,0.001137748,0.0002550371,0.00005716037,0.0004822969,0.001455566,0.005344036],"genre_scores_gemma":[0.9698799,0.0004127962,0.0252286,0.0001787893,0.00003586796,0.00005377917,0.0004481156,0.00002604927,0.003736071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01048239,"threshold_uncertainty_score":0.02084279,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1971246165","doi":"10.1109/tnet.2012.2194508","title":"FireCol: A Collaborative Protection Network for the Detection of Flooding DDoS Attacks","year":2012,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Networking","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":161,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Public Works and Government Services Canada; University of Waterloo","funders":"","keywords":"Denial-of-service attack; Botnet; Computer science; Computer security; Software deployment; Flooding (psychology); Application layer DDoS attack; Computer network; The Internet; Overhead (engineering); World Wide Web","authors":[{"name":"Jérôme François","is_ca":true},{"name":"Issam Aib","is_ca":true},{"name":"Raouf Boutaba","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03682731981442335,"gpt":0.2635017878676642,"spread":0.2266744680532408,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001537489,0.0008483855,0.0006753291,0.002299213,0.0007402615,0.0008214058,0.002234892,0.001161409,0.001923396],"category_scores_gemma":[0.003772643,0.0002914356,0.00033803,0.0009232857,0.0006742126,0.002179289,0.002673737,0.0007262218,0.0007982978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008235756,"about_ca_system_score_gemma":0.0009443482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002755829,"about_ca_topic_score_gemma":0.003142525,"domain_scores_codex":[0.9992736,0.0001671384,0.00002942729,0.0001673312,0.0002543918,0.0001080741],"domain_scores_gemma":[0.9979987,0.0006164756,0.000296715,0.0005825586,0.0002742438,0.0002312917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003289053,0.001192379,0.02092408,0.000464698,0.0003064646,0.0009613003,0.000481478,0.2068605,0.04800263,0.01655566,0.05394785,0.6470138],"study_design_scores_gemma":[0.000229814,0.0004639145,0.004282928,0.0000278681,0.00006133779,0.0005939036,0.00009069513,0.9506965,0.01732481,0.004990477,0.02117925,0.00005865057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1765529,0.001265073,0.7646034,0.0006885191,0.0002379751,0.0009281903,0.001587301,0.04413327,0.0100033],"genre_scores_gemma":[0.735772,0.000316641,0.2574082,0.0002046586,0.00007075714,0.0002985174,0.002241469,0.0003021968,0.00338554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002755829,"threshold_uncertainty_score":0.008131087,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2953448948","doi":"10.1007/978-3-030-25109-3_9","title":"A Detailed Analysis of the CICIDS2017 Data Set","year":2019,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":158,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Atlantic Canada Opportunities Agency","keywords":"Computer science; Intrusion detection system; Data mining; Metadata; Set (abstract data type); Network packet; The Internet; Random forest; Payload (computing); Attack model; Anomaly detection; Anomaly-based intrusion detection system; Machine learning; Computer security; World Wide Web","authors":[{"name":"Iman Sharafaldin","is_ca":true},{"name":"Arash Habibi Lashkari","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06541843500189172,"gpt":0.3043812116250212,"spread":0.2389627766231295,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001380904,0.0008005701,0.0007166174,0.004276318,0.00116513,0.001409126,0.001068684,0.000804599,0.006899511],"category_scores_gemma":[0.004661307,0.0002468125,0.001074301,0.005576622,0.0003381556,0.0006996312,0.0008275798,0.001083471,0.00640492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001230235,"about_ca_system_score_gemma":0.003284245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04734015,"about_ca_topic_score_gemma":0.07530948,"domain_scores_codex":[0.9977801,0.0001720463,0.0001657973,0.0002322562,0.00136986,0.0002798105],"domain_scores_gemma":[0.9979791,0.0004315366,0.0000975923,0.0004744441,0.0008882805,0.0001290673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000755317,0.0006255823,0.05197019,0.000954699,0.0002797348,0.0007323374,0.000255728,0.01432168,0.01330493,0.003703781,0.7857198,0.1273763],"study_design_scores_gemma":[0.000221183,0.0002928561,0.2261486,0.0002543271,0.0001524223,0.00116806,0.00117931,0.0393282,0.02258332,0.003335082,0.70517,0.0001666114],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1783212,0.001687806,0.005860634,0.001418639,0.0008953459,0.0005961072,0.7833627,0.004490152,0.02336732],"genre_scores_gemma":[0.07505053,0.0003963157,0.009495242,0.0002415616,0.00009603188,0.0002743812,0.9063177,0.0004905542,0.007637787],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04734015,"threshold_uncertainty_score":0.0941292,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4375852471","doi":"10.20944/preprints202305.0443.v1","title":"CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":158,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Internet of Things; Interoperability; Computer security; Spoofing attack; Denial-of-service attack; Benchmark (surveying); Scale (ratio); World Wide Web; The Internet","authors":[{"name":"Euclides Carlos Pinto Neto","is_ca":true},{"name":"Sajjad Dadkhah","is_ca":true},{"name":"Raphael Ferreira","is_ca":true},{"name":"Alireza Zohourian","is_ca":true},{"name":"Rongxing Lu","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07577793488374687,"gpt":0.3222210206975374,"spread":0.2464430858137906,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001884116,0.00229143,0.001157981,0.003823873,0.00127419,0.001884968,0.002656731,0.002918133,0.001718303],"category_scores_gemma":[0.007715269,0.0003881895,0.001454493,0.004172378,0.0009181433,0.002548509,0.001704548,0.002249399,0.00310773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001477499,"about_ca_system_score_gemma":0.001289312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01173656,"about_ca_topic_score_gemma":0.0144049,"domain_scores_codex":[0.997175,0.0003365752,0.0003756009,0.0006755769,0.001021878,0.0004153805],"domain_scores_gemma":[0.9957681,0.0009062183,0.0005324345,0.001361436,0.0009625002,0.0004694101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00175477,0.001345598,0.06027475,0.001835078,0.0007419127,0.002042715,0.000400744,0.08047282,0.01037774,0.004590911,0.7666921,0.06947093],"study_design_scores_gemma":[0.0006425604,0.0009213202,0.1491118,0.0003376047,0.0002941573,0.003964611,0.00112952,0.4765467,0.02041293,0.008557996,0.3377691,0.0003118203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2214469,0.003885573,0.02242462,0.003438594,0.001758979,0.001332194,0.6923428,0.03557605,0.01779414],"genre_scores_gemma":[0.1598808,0.0006465575,0.01663115,0.0004416404,0.000163144,0.0005338267,0.8194475,0.000661919,0.001593389],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01173656,"threshold_uncertainty_score":0.02333647,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2147066793","doi":"10.1109/tpwrd.2010.2050076","title":"An Intrusion Detection System for IEC61850 Automated Substations","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Kinectrics (Canada); Western University","funders":"","keywords":"Intrusion detection system; Address Resolution Protocol; Network packet; Computer security; Sniffing; Computer science; Host-based intrusion detection system; Process (computing); Protocol (science); Anomaly-based intrusion detection system; Packet analyzer; Intrusion; Computer network; Embedded system; Engineering; Real-time computing; The Internet; Internet Protocol; Intrusion prevention system; Operating system","authors":[{"name":"Upeka Premaratne","is_ca":true},{"name":"Jagath Samarabandu","is_ca":true},{"name":"T.S. Sidhu","is_ca":true},{"name":"Robert Beresh","is_ca":true},{"name":"Jiancheng Tan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008661334531429392,"gpt":0.2332676274716758,"spread":0.2246062929402464,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007618371,0.0004915115,0.0004857539,0.0007523441,0.0003862788,0.0005594915,0.0005853359,0.0004971703,0.001224047],"category_scores_gemma":[0.002228296,0.0001671471,0.0002219526,0.0003419401,0.0003020292,0.0008653534,0.0004133524,0.0004127002,0.0004373263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005809811,"about_ca_system_score_gemma":0.0004550647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000845421,"about_ca_topic_score_gemma":0.0006740685,"domain_scores_codex":[0.9988678,0.0002793209,0.0000997198,0.0001512213,0.0005479822,0.00005414283],"domain_scores_gemma":[0.9990269,0.0002896536,0.0001403713,0.0001666904,0.0003409635,0.0000354368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001015327,0.000545987,0.02376362,0.0003073199,0.0001974681,0.000585629,0.0006739244,0.0572966,0.1691454,0.01389515,0.008558685,0.7240149],"study_design_scores_gemma":[0.0001860902,0.001617226,0.01589263,0.00005812258,0.0001877125,0.001278583,0.0001017528,0.7614321,0.1680411,0.003684257,0.04742957,0.00009086703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2240013,0.0004998061,0.7459717,0.0003223082,0.0002415736,0.0005242953,0.0002524961,0.02050568,0.007681001],"genre_scores_gemma":[0.8610781,0.0001643823,0.1347436,0.0001311365,0.00004697575,0.0002056814,0.0003629096,0.00007847497,0.00318865],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001224047,"threshold_uncertainty_score":0.00421536,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2237959143","doi":"10.1016/j.eswa.2016.01.002","title":"Malicious sequential pattern mining for automatic malware detection","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Malware; Computer science; Executable; Data mining; Classifier (UML); Trojan; Cryptovirology; Intrusion detection system; Sequential Pattern Mining; System call; Artificial intelligence; Machine learning; Computer security; Operating system","authors":[{"name":"Yujie Fan","is_ca":false},{"name":"Yanfang Ye","is_ca":false},{"name":"Lifei Chen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01557217367219011,"gpt":0.2475662252126508,"spread":0.2319940515404607,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007711028,0.000710879,0.0007063558,0.003567757,0.0007455968,0.00079345,0.0008263281,0.0005710993,0.001496048],"category_scores_gemma":[0.003399845,0.0003444531,0.0008217377,0.001817767,0.0003519769,0.001003939,0.0005420207,0.0007320522,0.0007990344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003382063,"about_ca_system_score_gemma":0.0009793343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002223353,"about_ca_topic_score_gemma":0.004183549,"domain_scores_codex":[0.9990802,0.0001653188,0.0001152567,0.0002519789,0.0003118892,0.00007531647],"domain_scores_gemma":[0.9977629,0.001056455,0.0002462802,0.0003416286,0.0005023944,0.00009022326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004766662,0.0004336999,0.02041536,0.0003432747,0.0002433045,0.0006928627,0.0001694706,0.02902675,0.05657515,0.005295506,0.005479482,0.8808486],"study_design_scores_gemma":[0.0000187459,0.0001903452,0.004449356,0.0000268621,0.00008970998,0.0009468088,0.00006367661,0.9545246,0.02673277,0.009229061,0.003706674,0.00002127911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1631972,0.001334612,0.8262768,0.000258253,0.0001194921,0.0002317376,0.001137709,0.005365267,0.002078986],"genre_scores_gemma":[0.6103246,0.0004347964,0.3840964,0.0000845445,0.00007803326,0.0001425188,0.001894455,0.000152639,0.002791936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003567757,"threshold_uncertainty_score":0.005004764,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2793174642","doi":"10.1155/2018/4680867","title":"Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment","year":2018,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"University of British Columbia; China Scholarship Council; Quanzhou City Science and Technology Program; Huaqiao University","keywords":"Computer science; Cloud computing; Intrusion detection system; Decision tree; Fog computing; Data pre-processing; Preprocessor; Tree (set theory); Big data; Data mining; Bayesian network; Real-time computing; Artificial intelligence; Operating system","authors":[{"name":"Kai Peng","is_ca":false},{"name":"Victor C. M. Leung","is_ca":true},{"name":"Lixin Zheng","is_ca":false},{"name":"Shangguang Wang","is_ca":false},{"name":"Chao Huang","is_ca":false},{"name":"Tao Lin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03129439288886937,"gpt":0.2631083152096583,"spread":0.231813922320789,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001137088,0.0006125289,0.001007687,0.001562075,0.0008056202,0.001073117,0.0009128177,0.0005678133,0.0006087027],"category_scores_gemma":[0.002685517,0.0002442368,0.0006978223,0.001334561,0.0002548477,0.001616182,0.0006359951,0.0006320941,0.000227084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006874462,"about_ca_system_score_gemma":0.0009315459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004650232,"about_ca_topic_score_gemma":0.003793848,"domain_scores_codex":[0.9988145,0.0001997248,0.0001512432,0.0002850417,0.0004161649,0.0001333966],"domain_scores_gemma":[0.998759,0.0004739729,0.0001322679,0.0001227585,0.0004231793,0.00008882128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001147043,0.0009263327,0.03041011,0.0003379558,0.0003497584,0.001145432,0.0004315621,0.2384871,0.02589509,0.00542695,0.01165155,0.6837911],"study_design_scores_gemma":[0.00002046142,0.0001001567,0.002954121,0.000009214035,0.00002811654,0.0001202946,0.0000494176,0.9871715,0.006213455,0.002195334,0.001121072,0.0000168572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1961215,0.0006278898,0.7935134,0.0006317314,0.0002736537,0.0003065781,0.0009408027,0.005136641,0.002447751],"genre_scores_gemma":[0.8185846,0.0003876962,0.1776494,0.0002340378,0.00008396158,0.0001700338,0.001445899,0.0000484813,0.001395879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004650232,"threshold_uncertainty_score":0.009246349,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4402035331","doi":"10.1016/j.iot.2024.101351","title":"CICIoMT2024: A benchmark dataset for multi-protocol security assessment in IoMT","year":2024,"lang":"en","type":"article","venue":"Internet of Things","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":154,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Benchmark (surveying); Computer science; Protocol (science); Computer network; Medicine; Cartography; Geography","authors":[{"name":"Sajjad Dadkhah","is_ca":true},{"name":"Euclides Carlos Pinto Neto","is_ca":true},{"name":"Raphael Ferreira","is_ca":true},{"name":"Reginald Chukwuka Molokwu","is_ca":true},{"name":"Somayeh Sadeghi","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02903116329021174,"gpt":0.352207993563471,"spread":0.3231768302732593,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001869219,0.002995679,0.00120886,0.003514343,0.001287495,0.002070528,0.003565893,0.003321605,0.002842933],"category_scores_gemma":[0.006472934,0.000418649,0.001679522,0.004384867,0.0008507039,0.002235143,0.002117955,0.001896016,0.003293933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003169222,"about_ca_system_score_gemma":0.002078215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03112897,"about_ca_topic_score_gemma":0.03930932,"domain_scores_codex":[0.9969212,0.0004562474,0.0003690761,0.0006838708,0.001201756,0.0003678363],"domain_scores_gemma":[0.9964818,0.0008100632,0.0004387414,0.0009036227,0.00103475,0.000331095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001174575,0.001425212,0.03945173,0.002484859,0.0006076611,0.001037631,0.0002262938,0.05478833,0.005389022,0.003573813,0.8034431,0.08639766],"study_design_scores_gemma":[0.0007703338,0.001326914,0.1017745,0.0007690085,0.0004083382,0.002999223,0.0009612368,0.3071617,0.01873778,0.007368403,0.5573762,0.0003462431],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1609348,0.004730205,0.0151041,0.003001673,0.001037931,0.001438561,0.7823428,0.01315595,0.01825394],"genre_scores_gemma":[0.09120529,0.0005956027,0.01096041,0.0004606092,0.00009800335,0.0005990756,0.8929591,0.0002523189,0.00286958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03112897,"threshold_uncertainty_score":0.06189555,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2139562214","doi":"10.1109/ijcnn.2003.1223682","title":"On the capability of an SOM based intrusion detection system","year":2004,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"","keywords":"Intrusion detection system; Computer science; Data mining; Benchmark (surveying); Hierarchy; Anomaly-based intrusion detection system; Knowledge extraction; Feature (linguistics); Connection (principal bundle); Artificial intelligence; Machine learning; Engineering","authors":[{"name":"H. Güneş Kayacık","is_ca":true},{"name":"A. Nur Zincir‐Heywood","is_ca":true},{"name":"Malcolm I. Heywood","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009856584194892683,"gpt":0.2067213579738227,"spread":0.19686477377893,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007733738,0.0003682297,0.0004755224,0.0007324011,0.000396162,0.001335975,0.0007011045,0.000700656,0.001879143],"category_scores_gemma":[0.003341686,0.0002197454,0.0003082085,0.0006762214,0.0005241917,0.001852148,0.000751064,0.0005831295,0.000953474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003172915,"about_ca_system_score_gemma":0.0003486917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001247114,"about_ca_topic_score_gemma":0.001287456,"domain_scores_codex":[0.9996965,0.00005885453,0.00002166345,0.00006082641,0.0001252724,0.00003694286],"domain_scores_gemma":[0.9988436,0.0005471816,0.00006523369,0.0001694457,0.0003107502,0.00006376328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001544272,0.0003466805,0.01053338,0.0003517831,0.000204318,0.0006716377,0.0003546497,0.170071,0.1137421,0.04078031,0.007310596,0.6540892],"study_design_scores_gemma":[0.0000258239,0.0002322633,0.001854748,0.00002678406,0.00004121876,0.0003996792,0.00006614008,0.9573786,0.02079638,0.01324658,0.00590498,0.00002685891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2309072,0.0009213253,0.7328374,0.0009710586,0.0002441026,0.00009289916,0.0002959128,0.009601849,0.02412827],"genre_scores_gemma":[0.83509,0.0004947006,0.1584325,0.0001851047,0.0000719327,0.00005023575,0.0002631751,0.0001125366,0.005299642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001879143,"threshold_uncertainty_score":0.006286383,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4290996887","doi":"10.1109/icc45855.2022.9838780","title":"A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":148,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Computer science; Intrusion detection system; Transfer of learning; Convolutional neural network; Benchmark (surveying); Encryption; The Internet; Authentication (law); Deep learning; Hacker; Artificial intelligence; Computer security; Machine learning; Computer network","authors":[{"name":"Li Yang","is_ca":true},{"name":"Abdallah Shami","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05285260986608894,"gpt":0.2953889118977056,"spread":0.2425363020316166,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000546683,0.001038709,0.0007394683,0.0008184353,0.0003248113,0.0004708927,0.001410386,0.0006873558,0.001274386],"category_scores_gemma":[0.0008614648,0.0003455698,0.0005173935,0.000439861,0.0002219158,0.0008471322,0.0007288161,0.0008845549,0.0005500453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001367438,"about_ca_system_score_gemma":0.0008686789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01307302,"about_ca_topic_score_gemma":0.01162998,"domain_scores_codex":[0.9997209,0.0000235821,0.00001444567,0.0001062303,0.0000741566,0.00006078631],"domain_scores_gemma":[0.9997944,0.00002867667,0.00002891267,0.00002764352,0.0001044548,0.00001593907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005852334,0.0005172166,0.008022704,0.0001329991,0.0003226853,0.0003352916,0.00006107107,0.3040485,0.02998552,0.001998964,0.01527471,0.6387151],"study_design_scores_gemma":[0.000008270235,0.00006231194,0.0007164551,0.000003243978,0.00001696879,0.0000344676,0.000005270917,0.9919065,0.006255815,0.0002844046,0.0006982185,0.000007970323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3046592,0.001811274,0.6551852,0.0007210573,0.0007055023,0.0003897469,0.001517427,0.02703786,0.007972712],"genre_scores_gemma":[0.8761816,0.0003234474,0.1144051,0.0002876754,0.00006449488,0.0001977174,0.002076285,0.0001194476,0.006344378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01307302,"threshold_uncertainty_score":0.02599388,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}